Firefighter Sleep & Shift Schedules | Data, Wearables & Driving Real Change: Asst. Chief Mike Binney

Episode Description

Discover how fire service innovations in sleep research and wearable technology are transforming health outcomes and operational effectiveness. Assistant Chief Mike Binney shares insights from his groundbreaking research that led to policy changes and changes to shift start times. 

Key Topics

The journey from firefighter to researcher, blending data science with fire service realities

The comprehensive sleep study at Yale and its implications for responder health

How wearable technology like Whoop is being used to collect granular biometric data

Strategies for securing funding and building trust with fire department teams

The impact of shift scheduling and policy changes on sleep quality and recovery

The concept of "duty-related jet lag" caused by rapid time zone shifts in first responders

The importance of human context in interpreting hyper-granular data

Future trends in wearables, exposure tracking, and AI's role in fire service safety and health

Practical recommendations for improving sleep through environment and policy adjustments


Timestamps

00:00 - Introduction and the importance of sleep research in fire service

00:37 - Chief Binney's background: blending data science with firefighting

01:49 - The development of comprehensive sleep and health studies at Yale

02:09 - Starting the fire service journey during the financial crisis

02:50 - Building bridges: working with unions and leadership on wellness initiatives

03:45 - The hybrid Yale program: merging health informatics with fire service realities

04:54 - Turning data into action: challenges of translating science into policy

05:43 - The motivation behind research: giving firefighters more time with families

06:28 - The effects of shift schedules and the push for 9 a.m. start times

07:05 - Funding the study: grants, partnerships, and leveraging academic credibility

08:12 - Partnering with wearable companies like Whoop for real-time biometric data

09:03 - The evolution from pilot projects to large-scale data collection

10:09 - Ethics and ownership of biometric data in fire service research

11:22 - The power of one "yes": scaling the research from pilot to full department

12:17 - Measuring over a million data points and insights from the dataset

13:26 - The role of behavioral factors and self-perception in fatigue reporting

14:49 - How culture influences wellness and data acceptance among firefighters

15:52 - Quantifying the impact of scheduling on physiological outcomes

16:45 - The significance of community, trust, and leadership in successful studies

17:51 - Building relationships: collaboration with unions and departments

18:21 - The pilot study: designing, implementing, and iterating

19:48 - Integration of diverse data sources: biometric, call times, and health diaries

21:29 - Data privacy, IRB approval, and ownership of biometric data

23:16 - The importance of transparency and respecting member ownership

24:54 - Addressing the "big tech" fears and ethical concerns

26:13 - Supporting members through data literacy and internal champions

27:49 - Recognition and awards: the John P. Redmond Award

28:41 - The Denver fire service collaboration and shared regional goals

29:41 - Blinding study phases and ensuring data integrity

32:28 - Cultivating trust: the role of local leaders and ambassadors

34:39 - Metrics tracked: heart rate variability, sleep stages, and performance

37:34 - Quality of data: managing noisy, messy datasets

39:26 - Incorporating qualitative surveys, family impact, and perception

40:16 - Policy implications: changing work start times and fostering better sleep opportunities

44:04 - Challenges and balancing operational needs with sleep health

46:08 - Using tech tools like Google Maps API to personalize commutes

50:44 - The "duty-related jet lag" phenomenon and long-term health effects

55:45 - Practical tips: leveraging wearables, environment, and moderation

58:41 - Future of wearables, exposure tracking, and AI in fire service

64:22 - The importance of maintaining humanity amidst increasing data and automation

66:21 - Upcoming research publications and the importance of scientific rigor

68:05 - Connecting with Mike Binney for collaboration and insights


Resources & Links

Science To The Station: https://www.science2station.org/

West Metro Fire Rescue
Whoop Wearable Technology
Yale School of Public Health
Joel Billings' Emergency Services Sleep Diary
Tactical Sleep Rescue Program
American Public Health Association

Contact:
opstalk.wfd@gmail.com  

Eric Linnenburger
linkedin.com/in/eric-linnenburger
elinnenb@westminsterco.gov

Mike Binney
Email: mbinney@westmetrofire.org

Transcript

SPEAKER_00 0:00

One thing that was really interesting is we looked at an algorithmic value of sleep performance pre and post our shift change, the benefits that we saw to people's sleep performance extended out across the four day as well. Not only did changing to the 9 a.m. improve sleep performance while you were at work, because mathematically it had to. You were not waking up early and on both ends of your shift, but those benefits carried out across the four-day all the way to the very last day. The only day that the change wasn't statistically significant was your fourth day of four-day. The idea that it takes you a couple days to recover sleep-wise and algorithmically recovery-wise, those benefits actually lasted to make your four-day that much better from the get-go.

SPEAKER_01 0:46

Welcome to Ops Talk, where we explore the ideas driving the fire service forward and the people behind them. Today's guest has been doing that and then some. I'm your host, Eric Lindenberger, and today's guest is Assistant Chief Mike Benny from West Metro Fire Rescue in Colorado. Mike is a firefighter, a researcher, a father and a husband, and someone who has done the hard work of blending legit data and science with actual fire service realities to drive change. Mike completed his graduate work at Yale School of Public Health, where he conducted one of the most comprehensive sleep studies ever done with firefighters. He also got his undergrad at the University of Colorado, which we won't hold against him. We're going to try not to anyway. Go Rams. We're going to talk about the research, the technology behind it, the challenges of driving change from within, and where wearable technology is headed in the fire service. And I'm sure you're going to find we'll find a plenty of other tangents to explore. There really is no more important topic in the fire service and in the realm of all things health and performance and sleep. As always, if you're finding value in what we're doing, please like, subscribe, share the show with others. It really does help to get it to people who can use it. Mike Benny, welcome to the show. Thanks for having me.

SPEAKER_00 1:57

It's great to be here. And I'm sorry for how nerdy it's gonna get.

SPEAKER_01 2:02

Love it. Absolutely love it. Just really appreciate you being here. You're doing some amazing things out in the world. I'd love for you to just uh tell the audience a little bit more about your background, how you made it to the fire service and and how you made it to this research phase of your life.

SPEAKER_00 2:19

Yeah, so uh I guess I grew up in Colorado. Um half my family's from New Zealand. So uh we were back there for a couple weeks earlier this spring, took my girls there for the first time, which the flight was uh not as bad as I figured it would be. Everybody survived on nothing but uh, you know, snacks and fruit snacks and all kinds of things. We we did it. Um but uh yeah, grew up here in Colorado, did my undergrad in Boulder. Um, and then uh it was kind of during the financial crisis, and there wasn't there weren't a lot of other jobs available. Many of my other uh friends from school just kept going to school. Um, and my father-in-law uh was an engineer at uh Denver at Station 20 for a really long time. Um, so I did a ride along with them and I was like, oh my God, this is awesome. This is like everything that I want in a grown-up job without having to have a grown-up job. Um so uh I, you know, hired, uh was lucky enough to get hired at West Metro and have been there since. Um in the interim, I worked for Pride Mark and I drove a wheelchair car. I think that gives me great humbling perspective of you know where you start in this industry, how you learn, um, and how uh, you know, you can pile on all these different life experiences. Um, and they give you perspective and shape kind of what you want to do uh as you gain uh more tenure and more time uh in the fire service or in emergency services delivery. So um I did that. I worked uh at our Colfax station as a field instructor for a while, um, ran our EMS training divisions uh from a lieutenant spot uh, you know, when we were just trying to spool it up for the first time, made some great uh connections in the region, learned a lot from some brilliant people who had been doing it uh longer than me and had some great ideas. Um, and then moved back out onto the line and was there for you know many years and uh was a station captain and loved that. And then uh started, like you had mentioned, my graduate work um at Yale. It was a hybrid program. So we'd go out there every couple months and got really into kind of the health informatics side, which is not as scary as it sounds. I'm still convinced I don't really know what it means, but it's uh, you know, this idea of we can blend all these kind of disparate data sources to try and find signals within that can help at least guide kind of what we're doing from an evidence-based and then blending in with, you know, the um the the cultural aspect of how we do business and try and find a sweet spot that uh, you know, the numbers say we're doing the right thing, but also the people say we're doing the right thing. And that, you know, as I found, is probably the the most difficult thing is translating some of the hard science into actionable things and then, you know, building uh a coalition of people who are willing to try new ideas and then measuring what happens on the other side and you know, spreading that message to to other places. So again, the the the time at Yale was super cool because I was so different from everybody else who was in the in the program. There were just some amazing and brilliant people um who came from you know all over the world really and with so many different backgrounds. Um, it was you know humbling to have my little niche skill set and experience um and then take that, you know, kind of out on the road. So that's what I've been doing for the last, gosh, 20 years. And uh again, kind of refocusing what I do now to being a dad. And I've got young girls who are very uh enthusiastic for swimming. So we just drive around and do everything for that. So it's been, you know, a nice pivot uh from some of the hard sciences into, you know, really uh the beauty of having four days off a week to be a good dad and be present and you know, and working in that domain. So that's kind of where I come from. And uh, you know, part of the reason that drives this research is I am hoping, you know, as a battalion chief or an assistant chief, um, to be able to give people back their spouses, give people back this time with their family, because at 56 hours a week, we do ask so much of our firefighters um and our operational personnel that if there's a better way to do it that allows them to be more present um or you know, better parents or better spouses, or even have more time and bandwidth to take their kids to or their dogs to the dog park, then you know, that's uh you know, or ride their mountain bikes. That's what I'm hoping for.

SPEAKER_01 6:54

Yeah, you mentioned uh the four days off. I I used to have that too, and it was it was pretty amazing. I don't know how I found myself here, but um, we're here now, and you you've also done the 40-hour schedule too, right?

SPEAKER_00 7:08

Oh yeah, yep. I did that uh while I was on special assignment and the co-EMS captain um for uh while the project was ongoing because trying to do it it you know it would have turned into a seven days a week, eight days a week job. Yeah. So yeah.

SPEAKER_01 7:25

Yeah, and so what uh what brought you to Yale? How'd you find out about that opportunity?

SPEAKER_00 7:31

I had looked at a ton because I had just finished uh first round of graduate work in public policy at uh CU Denver, um, and that was in emergency management, and I had done my capstone project in uh I don't know if we're allowed to say brand names, but I had worked with Pulse Point and then Nrail up in Golden and done a full like geolocation serve survey where we mapped out uh where all the pulse point or where all their AEDs were on the campus, and then I built like this system that analyzed using all of their GIS data what the response radius would be to and from those AEDs, and then found where gaps were in their um, you know, how their campus was laid out to make sure that they had the best positioning of their AEDs and their stop the bleed kits and things like that. Um, so it kind of started with that, and I was like, wow, this is really cool. And that was kind of when I got into some of the wearable devices. Um, division chief uh Steve Azteltine, his daughter is a pro cyclist, and they were sponsored by Whoop. And so we reached out to them and we're like, oh, this would be really cool to um, you know, put some of these things on our firefighters as like a pilot study, hook up all of our different data systems to them and see, you know, what's going on. Um, and so I had reached out to the admissions director at Yale, and this is totally on a flyer. I was like, they're never even gonna email me back. I'm just some butthole from Colorado. And uh I presented them with this project of like, hey, we want to, you know, look at this. We'd like to scale it. I would like to do something in the public health realm that could then translate some of this newfound passion for informatics uh and turn it into something that, you know, it and it like every good idea, like it just starts on a note card and you're like, oh, wouldn't that be cool? And then all of a sudden you're four years later and you're like, oh my God, what have I done with my life? Um, you know, so I reached out to them with this idea and they were like, oh, this is awesome. Do you have a funder for it? And that's you know, something else that would be really cool to talk about is the idea of in these kind of trying financial times, how do you find money to build systems like this? And one of the um the fellows at Yale is also from Colorado, and he had connected me with the Colorado eHealth Innovation Fund, which was under the lieutenant governor's purview. And I was able to apply for funding for the pilot study uh and then more money through uh the division of fire prevention and control. They had a health and wellness grant. So we were able to stack a couple grants together and fund this first round with nothing but my my time at the fire department as being really the only cost to the district. Um, and then, you know, once you have a little bit of academic leverage, um, you know, it uh for whatever reason, when you have an email address that has Yield at the end, people tend to send you emails back. Um so that you know, that was a huge bit of privilege that I was able to kind of walk backwards into, um, undeserved at the time, and you know, put some ideas together, put a couple of you know, things on paper, and then all of a sudden we're funded. And it's like every classic thing. We're like, ah crap, the plane's in the air and we got to figure this thing out. Um, and so you know, we put band-aids and duct tape all over everything while during our pilot to actually see what kind of information we could squeeze out of the wearables. And then we have a brilliant data analyst at West Metro. Uh his name's Pierce. And uh then we hired uh another brilliant person, Jessica, with two C's. Um, and you know, they were able to start, you know, like an old-timey switchboard, start connecting all our different systems together, and then we're like, oh my God, this might be one of the most powerful data sets we've ever been able to build because we're layering in so much. Um, and so that's that's kind of how it started. But, you know, I I like I said, I approached the school with like this kind of cool idea, and that might, you know, might have been the thing that got me in. Um, but again, like it's one of those things where you're like, you don't really know until you take a shot, and you might hear, you know, 10 no's and all it takes is one yes. And next thing you know, the trains have left the station and this is your complete identity. Be careful what you wished for, right? Yeah.

SPEAKER_01 11:58

No, it's so true, and I I do. I just try to encourage everyone. Yeah, just you you have to take that shot and it and look look at you now. I mean, we're talking how many years later, and we'll we'll we'll circle back to to the research, but how how long you've been at it now?

SPEAKER_00 12:12

God, this started in 2024, like the very beginning of 2024 was when we first put people on the whoops as part of our pilot. Um, and it again, it was like this blend of this like wellness idea of because we looked at a ton of different wearables. Um you know, in 2023, we were looking at a a bunch of different brands. We were looking at what data you'd have access to. And part of the reason we went the direction we did was because we wanted to also integrate with a wellness platform. And that, you know, this is something we can also also talk about is the idea of like when you're asking people to exchange their biodata for something, you don't want it to just be for nothing on their end. So we wanted to find something would be a value to the firefighters, you know, in the name of the scientific project, but also can we give somebody something that's a good fitness tracker, a good um, you know, recovery score analyzer and things like that to help people give direction to their day and sleep and timing and things like that. So again, we've had the system up and running for a while. Um, I think I I looked last week. We have over 100,000 nights measured and over a million data points in the data set, um, which is everything from like call time, like longitudinal call times, like when are calls happening during your 48-hour shift. Is it day one? Is it day two? What happens, you know, when you're running EMS, EMS fire calls in that order. And so we have all this stuff still to dig through. Um, but that's you know, that's when it started, and that's you know, like I said, we're over a million data points at this point, and we don't even know, we haven't even scraped the stir surface of what's in there.

SPEAKER_01 13:51

That's amazing. Let's let's go back even further. Let's go back to the why behind this whole thing. What were you saying? What were you seeing um on the streets, you know, as a responder, but also seeing in those busy companies that gave you this idea to start studying this topic?

SPEAKER_00 14:08

I think part of it was, you know, working at our busy station on the ambulance. I think anybody who hasn't remembered driving home or has not felt present when they're home or felt like they were short with their spouse or short with their kids, people who are supplementing with caffeine and then the hospitals are loading all the EMS lounges with Celsius drinks and white monsters and you know what it's it's part of the culture. I'm not here to say anything bad about uncrestables or you know, the the base of my food pyramid for many years. Um, but it it was that idea that, you know, we have it it's this hyperbole of like, oh, we're all tired, but now we have a suggestion on what to do with it. Well, we can't do that. We don't have the data. And then when we have the data, well, the culture won't accept it. So there's this round around and nothing ever happens. So for me, it felt like we now have widely available biometric tools. We have data systems that are so granular that they've never existed this way before. We have a group of younger people who we're hiring that want to know why we've done things the way we've always done them. And they have access to every PubMed study or everything that's put on Google Scholar, and they're like, why the hell are we doing it this way? And so it was uh a desire to want to be able to answer those questions of why, tied in with my own experiences of I'm very tired. And every time that damn red light in the sky comes on in the middle of the night on the second day before I've even made my bed, I was like, why is it this way? And can we do something different? So, you know, tied in with all of those things, and now I'm in a new phase of my career where I have like that ability to take a breath and be like, you know what? I was at our hazmat station, was was a little bit slower when this project started. And I was like, I almost owe this to the people on the busy ambulances. I owe this to the people on the busy engines who don't have the time or bandwidth to put what's into a project like this that's necessary to start to measure. And then eventually, you know, I see this as just the first step in trying to make things better for the people who are out there doing it every day and doing it at an incredible volume. So, and with the requirements that we have of people nowadays between EHR requirements and you know, moving to NERES, which I think is a great thing down the road, um, you know, we're just asking so much more. Like our technology or our uh communications strategic planning team said a new firefighter has like 20-something different logins that they're responsible for. And I'm like, oh my God, can we at least do one thing to try and make life a little bit easier for people who have 20 logins just for work? So yeah, that's kind of where it all came from. And like I said, then once I had a breath to start thinking about things, this seemed like really the the best way that I could give back.

SPEAKER_01 17:02

Uh you mentioned the uncrustables and the white monsters. I think I might have one still in my bunker pants, you know, just from years ago that's it'd probably be nice and ripe, nice and warm, soft, you know, just the right texture. It's perfect. It's like a fine wine. It is. But you do you you just get used to it and and you just start, you you just roll with it and it it becomes normal. And then you know how our people are as well. The ones I I just had to do this recently with a couple that have been at our busy house. I think your ones is your busy house, and our ones is our busy house as well. And you can't pry those people out of there either. No, and e even if we wanted to really uh set up some sort of rotation for you know to to to protect them from themselves, like we can't do that. And because they love it, they've got pride in what they do. It's so much dang fun, especially when you're young to be at those stations, but you're not young forever, and and it starts to catch up at some point. So uh just it's really cool. It's really cool how you got there. And most people, you know, have a lot of we we're full of good ideas people in the in the fire service. And yes, I mean we we are so good. We've got ideas people, we've got lawyers, we've got everyone out there that can uh you know make all the decisions in the world. But the people that actually execute, that's what I'm trying to get to the heart of with a lot of what we're doing here is is how we get to execution. So I'd love to talk now about uh how you built this up. Like you you you took it from from concept and you started, I'm sure it evolved too. It was probably an evolving study the whole way, but how did you just how did you start to scratch the surface on it? How did you start to recruit people to help you with it? Uh, how did that process work?

SPEAKER_00 18:45

Well, it all started with that pilot study. I think we're funded for 12 people at first. Um, and we split up the whoops that we had and put half on people who were on admin duties, and we called that kind of our control group, and then put the other six out in the field in places that I knew were busy, but I didn't know at that point compared them to how busy they were versus you know a similar population. Granted, people who are in admin are usually a little bit older. Um, but it we knew that they were as close as we could get to a control group. Um, and within like a week, we're like, oh shit, like these people, these groups of people are so different uh that we then had what we needed to approach the big grant funder and say, here's just our pilot data from like a month's worth of data. We can tell from a statistical perspective that these people have comp they're completely different populations when it comes to algorithmic sleep scores, recovery scores. This is out of whack. And now we almost have like a duty to investigate this further. And that's when we got our big check, uh, thankfully, and we were able to scale it uh to uh all of our line people who wanted to do it. So I think we ended up getting funded for almost over 200 uh whoops at first, and then got them out uh to the crews. And at that point, we still had the parallel, we were running in parallel with the pilot group. So during that time, we almost used it as like a beat lab to figure out all right, how do we pull through API? Like, how do we pull all of this super granular health data or biometric data? And now we, Pierce, uh, again, my hero, uh, was able to build this entire system architecture, and we're able to mush our ESO data, our vector staffing data, um, the biometric data. At that same time, we're also using Dr. Joel Billings' um his emergency services sleep diary. So we could look for a mismatch between people's perception of how they felt and what their body was actually telling them, and then layering in how many calls they actually ran. Or were you on a 72, or were you on, you know, uh work a trade, then have a day off, then work a 72? Like, what does that do? And that all lives in this data set. So we kind of just tested all these systems to figure out how we would mush them all together into one computer system. It felt like that part on Zoolander where they're like the files are in the computer, we're just smashing stuff in there and seeing how we could get all these computer systems to talk. And then once we had it, we started to write the code to analyze it and do some of the more high-level higher-level statistical analyses to start parsing out and cleaning the data. That's one thing that's super cool about this data set is we're actually gonna give it to the Yale School of Public Health so they can have their new biostatisticians train on it because it's such a dirty data set since there's so much crap in there. Like it's gonna be brilliant because you can't, I mean, you couldn't make something any worse than what we were able to deliver because there's so much in there and so much, you know, so much noise uh in that data set. So we really That first grant, we got the funding, we got it out. Then we had to educate everybody and why we were going to be super creepy. And at the time, like this was coming off of COVID. And I'm sure your department was like our department, which was like the rest of the world, where people didn't trust big tech. They didn't think that, you know, like, oh, we're, you know, we want to put a microchip on your wrist and watch everything you do 24-7. Um, so there was, you know, there was that angle. And what came from it was like we really got into the ethical nature of, you know, what some people might call biosurveillance, which is literally what it is. And so during that semester of school, I was actually working as a teaching fellow um in the bioethics department. Um, and so in that process, I was able to get real close with a couple of the professors there and talk about the project and figure out how the best way to partition the data and seal it off and who had access to it. That was one of the beauty uh beauties of the project itself is what it went through, like the super rigorous uh independent review board or the IRB process, where they have people whose sole job is to just send me back mean emails and tell me everything that I'm doing wrong with the project. So that way we knew when it came out in the other end, it was squeaky clean. And one of those things that we worked through was who would own the legacy data, you know, ownership of the legacy ownership of the data. And while we were consenting people, one thing that was super important was, you know, who had access to it. We didn't want to make operational decisions on who was fit for work at any given time, the can of worms that that would open. Um, so we uh, you know, we had a really robust flowchart of where the data would go, who would have access to it. Ultimately, um we collectively decided the best way to do it would be for the union to own our membership, owns the data set. And I can't tell you, you know, I've had private companies reach out and they're like, oh, we want to train our algorithms on your data set. And nope, like it's not mine to give. Like this is um, you know, this is all of ours. And our e board is the one who has the the voting decision on who would have access and who has access to what and what we do with it, with the idea that this would only be done to advance health and wellness. Um, you know, you get an insurance company sniffing around saying, like, oh, we'd really like to know what's going on in there. And or, you know, people trying to build their own algorithms to create these wellness platforms. Like it's all, you know, it's super valuable. I would say like the biodata is probably the most valuable part of this whole project. And so it was critical to me, it was critical to our membership to have it clearly defined what we'd use it for, what we won't use it for, who owns it, who sees it. And, you know, because of uh the granularity of the data, we partitioned it off so there wouldn't be anybody who would have full access to the entirety of the data set at any given time. You would basically ping the data set with an inquiry and it would process it, you'd get your methodology, and then it would come out the other end with what you were looking at. So it at no point does everything exist in the same point at the same place at the same time. So that was, you know, part of building buy-in was being crystal clear with what this was and what it wasn't. You know, there isn't you can't see what the granularity with somebody does between 10 and 3 a.m. if unless you're you know on if you have that access um that that nobody had at uh at one point. So that was the big part, the consenting part, we made it all uh available to everybody. We said at any point, if you're not comfortable with it, we'll give you your own data. We've had some people who have reached out, and this might be in the weeds a bit, but some people have actually reached out, asked for their data and taken it to their primary care doc uh to look for different things. So that was all another part that I'm really interested in is tying it in with our annual physical data. So we use frontline. Um and uh I've talked to Mike Connor there, and he's like, man, the the the fact that you have not just point-in-time data, you're like, oh, we draw your labs once a year, or we make you do a bicycle test, a VO2 max test once a year, like that's just reactive at that point, where somebody looks disappointed at you and says, Oh, you need to do 10 hours of zone two every day. And like, so you know, that's uh the where I see the frontier of this going is like, you know, we have we'll have access to longitudinal data and not just point in time data.

SPEAKER_01 26:29

You mentioned something there, and it was it was brilliant that you you know you teamed up with the union, and and those relationships must have been there beforehand in order to have that on board. And and I think that that just it's it's just a good lesson, you know. We talk a lot about you know the importance of collaboration and relationships on here, and I always shout it from the rooftops whenever I can. And um, you wouldn't have that ability if you didn't already have that established, I would imagine.

SPEAKER_00 26:54

Well, and yeah, I mean, this is just yet another example of how great our our um our local and administration work together because again, like you can build this thing, but if admin isn't willing to enact a policy that's data informed, and the other way around is if we didn't have buy-in from the membership, then nobody's gonna wear these dumb things. And so you're never gonna learn anything that was so I I do think that that relationship, you know, is not uh you it's not unique in the Denver metro area. Um, it was uh, you know, I think everybody does a great job. It just really does take everybody seeing what the the end goal is. Like admin can see that there are efficiencies within what we're doing, and you can feel validated in making those policy moves. And the membership can feel like they're supported, and the the collective aggregation of the biodata tells a better story than other, you know, any ding dong with a PowerPoint talking about UHU will ever be able to come up with.

SPEAKER_01 27:51

Well, and I think it's it's probably a a good time to mention too um just how well supported this was on both sides, and so much so that you got some national recognition as well, right? You you won the John uh the John P. Redmond Award, which is pretty pretty amazing. Uh that's that's an international uh accolade, and just congratulations on that and and well deserved.

SPEAKER_00 28:12

It's cool. Like I I had mentioned previously, there's so much neat stuff going around uh, you know, internationally even in the health and wellness space. Um, you know, it's super humbling to get it. None of it happens without the support of our members and you know, especially the chiefs who let me kind of dabble in all this random thing that was uh started as a side hustle and now like it's like you know, a second 40-hour job a week that I get to do. But, you know, I think it's just a reflection of the you know, that award is amazing for the individual, but it doesn't mean anything without everybody who participated. Otherwise, you know, like I said, I'm just some guy wearing a whoop. But uh you know, it's more than that. I'm happy about it.

SPEAKER_01 28:54

It's more than that. But I think it, you know, I'm I'm proud of you for it and proud of us. You know, it's our region, I think, is really strong in a lot of the work that we're doing. I think we're leading the charge. And I know there's great work going on across the country, not to take anything away from that, but I'm really proud to work where we do. Um and just for for reference, uh, you guys are on the kind of the west side of Denver. We're more uh northwest, and uh we're not we don't border each other, but uh I know we've got a bunch of weird paths a lot. Weird white fire trucks in between us. We do, yeah. Uh so yeah, so I'd love to get into a little bit, we don't have to go deep, but you started to talk a little bit about how you got the firefighters on board. Um this was a tricky, tricky time, especially. Uh trust wasn't at its highest uh level that it has been, and and you're putting these tracking devices on people. Uh how did you do this? And I think you blinded the study in the beginning, right?

SPEAKER_00 29:53

Yeah, so we didn't want any of the interventions from the Whoop to change people's behavior. Like, granted, you would know there's some, you know, there are a couple different biases that exist in the background when you're like, oh, somebody's watching me, I better go to bed early.

SPEAKER_01 30:09

Um I've got two two shamers on right here. And yeah, and and they said I wasn't doing enough. And so then I went down and I I rode uh the Peloton, which also gave me an additional um streak shaming, you know, mechanism. So I've got three shaming devices all piling on one another, and and and they do, they change your behaviors.

SPEAKER_00 30:28

So yeah, so so it was partially like we we had the grant written, we wanted to do some blinded science at first, and with the whoop, it takes a couple weeks for it to calibrate to your baseline. Um so we started everybody in September of September of 24 was the the big scale up. Um, and we blinded everybody during that calibration phase because we were collecting raw data in the background, not algorithmic data. So the raw data wasn't impacted by the algorithms as they learned your your habits, your body, um, you know, your exercise habits and things like that. Part of getting people to participate was also offering something on the other side. So once they were blinded during the baseline period, um, we unblinded everybody and then gave everybody access to the Whoop and all of its wellness platform stuff uh for the rest of the year for that contract. So it was like a couple months of blinding, and then you got, you know, 10, 11 months of um of using it as if you know you were a Patrick Mahomes or you know, F1 racer or somebody, you know, one of their headline Ronaldo. We'll go with World Cup Ronaldo right now. There you go. Good job. Um yeah, so it by offering that blend of like, yeah, we're gonna do some stuff in the background, but now you get this tool that we were eventually able to validate as a wellness tool. Um, you know, uh, that was what that's what it took, plus, you know, being completely transparent with the data architecture, what we were doing, what were you looking for, what the end goals were, what it would be used for. Um, all of those things, you know, I think improved. And again, when you're looking at wellness stuff, you need to have somebody who has some political capital to give. So when we did the initial pilot, we targeted some people who had a lot of street cred, had a lot of respect within the fire stations to say, like, this is why we're doing it, and this is why I'm doing it. We almost had our own internal brand ambassadors. And then once people are like, oh, what's that? Uh, you know, then we were able to have the conversation and had great participation um throughout the whole program. So yeah, it was, you know, coalition building within. It was finding people who had Gravitas and street cred within the district, and then using those people to kind of amplify what the tool was and how we were going to use the information.

SPEAKER_01 32:56

Yeah, it's firefighters are are uh a funny group, right? We uh if you do get those champions on board, we will we'll believe everything they're saying, like gospel. We we get on board, we're a team, we we instantly form those trust and the trust in those teams, but man, we're also cynical on the other side and uh not a lot of trust outside of of our circles. So very, very smart, very good.

SPEAKER_00 33:22

Well that was to do it. And that was also part of during the pilot program, we debugged everything. We found anything that could go sideways because as soon as you can uh you know gain gain like a positive response, it's so much easier to lose it. You have one thing go sideways, one person, you know, all of a sudden have a bad experience with either the whoop or the system or anything that is, you know, downstream of the system we built, you know, I'm thinking like, oh, we had somebody who was in the red and now, you know, somebody's trying to send them home, then all of a sudden the entire system goes dark. Everybody takes them off and throws them away. So that's where, you know, when I talk about building programs and things like that, it's gaining the political capital to be able to ask people to do things that they weren't, they hadn't thought of before, they might be uncomfortable with. It's giving them context, it's giving them the reason behind it, empowering people with their own information, and then also like demonstrating what the value will be to them on the other end for participating. And so I think that you know translates into a lot of different stuff that we do at the fire department is we're asking people to adopt different technologies. And ultimately, I think it makes us more competitive with the younger people who are native to this stuff, and uh, you know, and that they're open to these changes and these ideas because they have lived in this kind of uh, you know, show me, don't just tell me what you're gonna do, show me why we're doing what we're doing.

SPEAKER_01 34:49

What what metrics were you tracking? Because I know you were tracking more than just time, time in bed, time asleep. What what are the the important metrics you were tracking?

SPEAKER_00 34:58

So with the Whoop, you can pull resting heart rate, um, heart rate variability, you're able to pull all the algorithmic stuff um for sleep performance, sleep consistency, recovery percentage. Those are kind of the big algorithmic ones. Um, you can take pulse ox, you can take uh, you know, uh body temperature, things like that. Uh it's also a really good performance tracker. So you can get down to you know how much time during a workout was somebody in zones zero through five, and you can have people start uh a whoop activity during or a workout during a multi-company drill or a live fire burn and things like that. And we could start to look at from a time series perspective, how much time are people spending in different heart rate zones? And that's kind of cool because it calibrates to the individual, because a zone four for somebody might be a zone five for somebody who's less conditioned. Um and same thing when you're looking about um, you know, over a lot overall physiological impact of the day-to-day in the fire station. You know, we use UHU as kind of the gold standard of busyness. And what we found when we compared some of our physiological markers to UHU, the busier you got, the less predictive UHU was, the less accurate UHU was. So for our really busy rigs that also had high UHU, it was still under-reporting or under-guessing what the or under measuring, sorry, um, what somebody's physiological response was to it. Because those are the crews that are also training and those numbers don't show up. Those are the crews that are out doing BIs, those are the crews that are, you know, driving back from the emergency room at three in the morning and then catch another call on the other end. But that time between dispatch and closing out at the ambulance is never counted as part of that UHU. So there's a there's a lot to it that I think the metrics gave us a lot more context to the story of what was happening rather than the data systems that we initially had available to us. So yes, we were able to pull algorithmic stuff. Yes, we're be able to pull like granular biodata, um, but without context, without the call data and things like that, and without being able to build out uh workouts and things like that, it it gave us a lot more perspective into what what the heck the numbers even meant.

SPEAKER_01 37:19

Well, and that you know, there is so much nuance to it that the on-seen times are going to be different for a for a medic unit that's it is just turn and call after call versus uh versus that truck company that's sitting on a gas leak or or an engine company. And so it's just remarkable that you were able to pull this dirty, kind of messy data together and uh make some sense of it. It's impressive. Very much so. It's very cool. If you're into that kind of stuff. If you're into that, yeah, absolutely. Uh but yeah, you you were able to quantify uh some of the things we've probably all been thinking for for years and years. Uh did you uh you also brought in some qualitative data though, didn't you? Didn't did you do some surveys with it?

SPEAKER_00 38:01

Yeah, so we used Joel's um his survey that asks, you know, what's your level of fatigue? What's your uh what's your perception of how her hard you worked yesterday? What would you say your drowsiness, you know, all kind of the qualitative end. And we were able to look at and compare people routinely under-report how how fatigued they feel. And that might be because of the white monsters. It might be because of the way that you're, you know, your baseline, your readiness to respond at any given time while you're at work, you're at a heightened state of alert. And so you might under-report, nope, I'm good to go, you know, how many times you know you get crushed the night before, and you're like, nope, we're good. Keep us in service, we're fine. And so people would under-report their perception of their um their fatigue when you compared it to their physiological markers. And that was really interesting. So we're also a uh uh test site for Sarah Janke and Brittany um uh Hollerbach and uh Joel's NDRI, their uh FEMA funded study, they kind of just like carbon-copied our methodology, and now they're looking at a bunch of different departments across the country and looking at how shift change time or a uh schedule change actually impacts people using this biometric platform. Um, and one thing that they've added into their methodology, which is brilliant, is they're actually doing uh long survey or free response surveys and they're talking to families and they're saying, how did this impact, how did this change impact your person at home? How did this impact how you felt uh they were present? Um, and then adding in some of that qualitative layer, because again, we can, you know, slice and dice 56 hours any way you want it, but at the end of the day, if you're not a better person coming home, or if it makes more work for your spouse or it makes you less present for your kids, then you know, extra 90 minutes of sleep doesn't save the day. It just pisses everybody else off in your life more.

SPEAKER_01 40:09

So true. So let's talk about that then. Uh you you had some recommendations that came out of it. You saw some pretty clear patterns, right? With uh consistent sleep. What what were the the main recommendations that came out of out of your work?

SPEAKER_00 40:26

So the big things we learned from a policy perspective, we were hindering people's sleep opportunity. Um and that was uh probably the biggest, most glaring thing on the other side. We looked at, you know, all these different uh sleep stages and things like that. And there's a lot of noise within those. But the one thing we could definitively say is people we were making people wake up early to commute to work, and we were making people wake up early to go home. Um those were the glaring differences at the first step. We were like, oh, this is a policy level thing. This isn't, you know, I think of things as like uh system level, like um the way we do business, the human choice level, like what do people have agency to change in their own lives? And then policy level, what can the fire department change to give you the opportunity to make better s decisions? And how does the system change to help support those things? And so we it was pretty evident that kind of the ergonomics of the way we did the 4896 left a lot of sleep on the table for lack of better terms. So we noticed that people were waking up over an hour earlier than their naturalistic baseline. So when people were unconstrained, constrained versus kind of unconstrained sleep were the two domains we looked at was when you were unconstrained, people woke up around 7:30 every morning. Granted, they didn't have, you know, toddlers coughing in their eyes or other things going on in their lives that made that uh change that. You can't control for that. But we were forcing people to wake up, you know, before 6 a.m. every day to drive to work. Same thing the other direction. Um so we saw that would be a good opportunity. So we looked at and our union um went through the process of doing a vote to see if anyone would be interested in changing start times. And that was kind of the first, the first step. And we uh the membership was supportive of that. And then we did a two-layer vote of if you are in support of it, um, what would uh, you know, what times would be appealing to you? And we offered 8 and 9 a.m. Um as the two choices. Now there's a whole bunch of other choices out there. We kind of, again, as everything, we're finding compromise. And we uh as we're spooling it up, uh, Chief Lombardi at the time, uh, he approached every different division and said, How would these changes impact the way you guys do business? And training center, fleet, support services, all the facilities people, um, some of our partners in EMS training, some of our regional partners, how would this impact Tazmat training? Uh, things like that. So it uh we really came down to, you know, these are the two things that everybody realized or agreed that we'd be willing to try. Like our district chiefs, myself, we start at seven o'clock at night the night before our normal crews come in. And uh that wasn't one of the options that the group was willing to entertain, um, which is fine. And so uh then most people voted in support of the 9 a.m. And so right now, uh Chief Metz, he was like, all right, let's give it a go, uh, which is awesome, which then gave us the opportunity to use this system to then measure the physiological differences between what we were doing before and where we are now. And so that was kind of the roadmap to how we ended up at nine. Um, and that was our intervention, and we were able to measure it. And now we're in the process of figuring out. What do we do next?

SPEAKER_01 44:01

I imagine there were probably opinions all over the board of where people wanted to start. I bet there were a lot that probably wanted to start a little bit later. But I wonder, can you talk about that a little bit? Uh, because when you did go survey the different divisions, um, you know, I I start to think about that, you know, when we start talking about these things to our different areas. First, the first thing that always comes up is training. You know, how are we gonna because as you talked about, we have more that we're responsible for than ever before. We have so much to cover. So, how do we find that balance of of reaping the benefits of the work that you've done and and and the sleep that we need, but also get get the work done. I imagine that was a factor for you guys when you were looking at this.

SPEAKER_00 44:43

Yeah, very much so. I think, you know, because we are part of a collaborative regional academy. So we've got you know, Monument, Castle Rock, um, Arvada, you know, everybody who has um so we needed to be conscientious of that. Um, I, you know, I talked to Toronto Fire when we were in the process of it, and they have a city fleet department. And so they're like, wait a minute, if we start any later, then we're gonna miss B services for garbage trucks and buses and school buses and all this stuff. Fire trucks are the divas of the whole system, right? But they're we're talking, you know, hundreds of thousands of dollars in dead time if they can't get the fire trucks in early. So there are all these other things that you don't think about. And that's why I think it's so important to custom tailor what's best for your district, what's best for your people, what's, you know, yeah, we can we're missing, are we missing pub ed events? Are we not doing crossing guard PR stuff because uh, you know, it's the second morning and people are ready to go home and we're like, oh, we're gonna go to an elementary school first thing in the morning. So there are all these different balances that you have to consider that are specific to your own district. Like one of the big things is you know, in the metro area is traffic. And so one really cool thing that we did, one of uh uh one of our firefighters, his daughter was a senior up in your neck of the woods up at um legacy high school, and she needed to do a data science project. And so uh we got together, and her project ended up being she and I worked together to build the computer code that would tap the Google Maps API, and we built a dashboard where every firefighter at the district could type in their home address and what station they wanted to go to, and it would model the entire drive, give them all the different routes they could take, what time they needed to leave, what their estimated sleep opportunity increase would be. So part of that building buy-in was actually using this platform that she and I developed, and it was mostly her because she's brilliant. Um, and uh, you know, the to give people individualized information to help them make the best possible decision for themselves. So we factored in the drive time, we looked at all these different things, and then at the end of the day, we were able to present like what I thought was a pretty buttoned up, um, you know, pretty well thought out policy change that gave people their individualized information. We looked at every division, we looked at the best research that we had available to us at the time, and then we had the ability to measure it on the other end to see if what we did even did anything. So that's how it worked out. And it, you know, like I said, we're a couple months into it, and we have finished a lot of the data analysis or analysis on the other end, and it did it did what we thought it would. Um, so that was really cool.

SPEAKER_01 47:34

Yeah, I'm glad you brought up that uh that work that you guys did with the commutes. I I when I heard you present this, you had her with you. And wow, what an what an impressive human, right? Yeah, super, super smart and just motivated and and I don't know, just uh years beyond her age, really. You know, like pretty impressive, pretty cool addition.

SPEAKER_00 47:57

She got into uh George Mason University and their exercise science group. Um, and she is leaving later this summer. She's doing a semester in Spain. And I'm like, oh my gosh, that's so cool. What a time. What a fun time in life to be at the beginning. And so much, you know, she took a whole bunch of coding classes at the uh University of Michigan's open campus, so for free, and she took her human subjects research certification class as part of her program. And, you know, hopefully that's like I've always said along the way, like it it's cool for me to know this, but if we're not helping the support and growth of other people, uh then it ends with me. And that's the last thing I want. So that's why I think so much of what we're doing is hopefully opening up people's eyes to like what kinds of things are possible. We're bringing people along whether they want to or not, and we're giving other people the opportunity to build on this. So when they look back, they're like, oh my God, that guy was so dumb. I can't believe he thought this way.

SPEAKER_01 48:55

Very, very impressive. I that girl is going places. I'm gonna tell you that right now. Yep.

SPEAKER_00 48:58

Uh it was nice to know her when she was a regular person. Right.

SPEAKER_01 49:01

Yeah. Just cool though to very, very cool to bring bring someone in from the outside. I think we need to be doing that more often. We need to let people in. This is this can be kind of an intimidating environment for people. And and frankly, we're just we don't have the tools to do it all ourselves anyway. So that collaboration is is massive. So you landed on you landed on 9 a.m. Uh is there a plan to to reevaluate, to adjust that down the road? Uh I don't know that there is.

SPEAKER_00 49:32

I think we've done the first step. Uh you know, there there's all the conversation in the region about people wanting to change times and things like that. Like, I think it's a nice stopover because it'll allow other people to kind of catch up. It'll allow training calendars and things like that to start to sync. I think we've been able to 9 a.m. pretty much maximized sleep opportunity. Because if you go much later, you start to impact other parts. You know, you don't want to be on your 48-hour because you know, call volume starts to go up around then, other things that we ask people to do start to go up around then. Where I think if we went a ton later, we would start having like a diminishing returns on the effort that it took to get it done. It would, and so uh by no means, I mean, I think we should think about everything, but I I think like at least for now, this is a good win. We'll take a breath. We've normalized one really cool thing that we learned in this project is even at 9 a.m., relative to the commutes, the middle group of people, um, we were able to stabilize people's um wake up time. So we were able to get real close to people's biological wake up time and make that consistent across their entire six-day cycle, which is what we, you know, that was the entire intent of the project uh or the idea from the beginning was it, you know, one interesting thing that uh it when we go back to kind of the personal ownership of making good choices and decisions, um, you know, I had a I met with a group that was they had somebody who was kind of, you know, playing the the counterpoint to all of these things. And this person brought up the idea of, well, people just need to go to bed earlier. They need to make better choices. Like, yeah, you if you know you have to get up at five in the morning to drive to work, you should be going to bed earlier. And, you know, that my my comeback or my retort to that was like, man, we have over 100,000 nights of sleep that we've measured. And people will make small changes at little points of time, but they will be right back to way they were before too long. And it normally, like, no matter what the intervention was, whether we started at 7 a.m. or 9 a.m., people went to sleep at the exact same time, whether they were at work or whether they were at home, when they were in those constrained or unconstrained windows, everybody has like their own story or their own anecdote or their own whatever. But we have 100,000 sleeps that say as good as your policy intervention could be, as important as your personal ownership is in this whole deal, people will be back to their nonsense no matter what you do. It's that 10:30 to 11 o'clock window is where everybody's going to sleep, no matter what we do. But now we have the data to prove it, the data to say it, the data to inform it.

SPEAKER_01 52:27

That is good information. I can still, I mean, it's only been a couple years since I was on the 48, and I can still remember I mean since the beginning of beef, even before we went to the 48-hour shift, uh, just that night before, uh, the sleep debt you're creating, uh, when you when you you finally catch up during the four day, and then you you try to do all the right things. You you try and and your body just won't let you, and then you're right back into that cycle again. I mean, I get the same thing a little bit now uh working the 40 hour shift, even you just because I do have a a pretty substantial commute, so I'm up early to get on the road. Um but it is hard. It's even hard on a Sunday night. I would love for you to you you touched on it, you touched on the policy change, you started to talk about the human choice component. What can we, regardless of shift change times, based on the the research you've done, put into play today to improve our sleep?

SPEAKER_00 53:23

Well, trackers work. As you mentioned, you've got three uh tiny little Catholic priests in your house trying to make you feel guilty about everything you're doing. We were able to validate that if you used it as a uh sleep aid or sleep information tool, um, people who stuck with it for six months, they were actually able to improve uh their sleep metrics, their sleep performance. So if you interact with a tool and you stick with it, uh it does work. We were able to validate that. Um we have also worked with uh a group called um Tactical Sleep Rescue. Uh, one of the firefighters who developed it, she's from up in Oregon. And there's it's a really cool, it's like a 30-day program where you can progressively go through these evidence-based things to create what she calls like a sleep stack. And that's everything from some supplementation, it's you know, turning your phone off, it's doing um all of these things that you slowly build towards making better choices. And again, it goes back to it's only as good as the the effort you're willing to put in. And you look absolutely ridiculous with the blue light glasses on. Um, but you know, there's evidence behind using those and not, you know, putting the red shift on your computer screens, um, using different wavelength or different Kelvin scale lighting in your bedroom so that you're not using operation room, you know, operation room blue uh before you go to bed. You're using more red wave lights. There are things you can do when you look at station design, when you look at your own sleep environment at home. Um, you know, so that really helped give context and like buildable things that you could scale and you could stack uh to help you get the best sleep that you can in those unconstrained periods. And that that goes to say, man, that in this whole project, like you also have to live. Like, you know, we we had one group of guys go out to the Rockies game, and they all were in the program and they went out, you know, full news team, and they came back and they're like, Oh my god, this ruined my sleep for three days, because you pretty much have to drink to watch the Rockies. Of course you do. Um, but it it ruined their sleep for like three days, and they're like, I don't know why I'm doing this. And I'm like, man, you this isn't like a sales pitch that you need to live the Puritan lifestyle. It's that there's moderation, and if you can measure it, then when it's time to make you know the choice between the 11th and 12th beer, maybe you should have stopped a couple minutes a little bit earlier. Uh, so it does it it it opens those ideas. It's the idea that you can you you still need to live, you still want to be present, you still want to have a social life, you still want to do awesome things, but those things can be data informed now. And now you can make that kind of analysis of the trade-offs you make to go to a Rockies game and then stay home and, you know, watch uh, you know, another episode of something on Netflix and go to bed at 9.45.

SPEAKER_01 56:29

Is there anything that uh that you found that you were surprised by or that you didn't find that you were surprised by?

SPEAKER_00 56:36

One thing that was really interesting is we looked at an algorithmic value of sleep performance. Uh, and when we pulled it pre and post our shift change, the benefits that we saw to people's sleep performance extended out across the four day as well. So looking specifically at um that metric, not only did changing to the 9 a.m. improve sleep performance while you were at work, because mathematically it had to. You were not waking up early and on both ends of your shift, but those benefits carried out across the four-day all the way to the very last day. The only day that the change wasn't statistically significant was your fourth day of four day. Um, so it, you know, the idea that it takes you a couple days to recover sleep-wise and algorithmically recovery-wise, which is another talk, um, it those benefits actually lasted to make your four-day that much better from the get-go. So, you know, people had better performance on those three days right after. Their recovery also didn't see that huge dip, the huge knock from a heart rate variability standpoint, things like that, when the insult of being at work and having those disrupted sleep. So not only was the policy change able to improve sleep in the constrained environment, but also carry through onto that whole four-day. So doing this actually made four days better too, which was interesting that we were able to tie a number to it. Um, one concept that came out of it, because it doesn't exactly exist in the literature right now, is the idea of social jet lag, where the choices you make on your weekends or your unconstrained sleep almost put you into a different time zone. And so myself and um Mike Winniger, one of the PhD biostats guys that's helping me on producing like the actual academic literature on this on the other end. We kind of coined this idea of duty-related jet lag, where when you're in when you're waking up early to go to work, you're actually forcing yourself to be in a different time zone. And then you're starting to acclimate over those two days. Now you come back, you reintegrate into your life, and you're already coming back to a different time zone. And we're yo-yoing these time zones. You know, when you look at the the delta between when you were waking up for a 7 a.m. shift start and a 9 a.m., that delta is somewhere in the hour, hour and a half range is we're basically forcing people to go to uh central time zone or eastern time zone for work and then recommuting back to mountain time zone and then living their lives and doing that for a 30-year career. You wonder what that the long-term impacts of that kind of dysregulation is. So that was another thing that surprised me is that it just doesn't really exist in the first responder literature this idea of this jet lag that we're basically creating from a policy perspective. So there'll be a lot more that will come out of that over the next year or so, once we get more numbers to it. But it was staggering that that it's like a a duty induced jet lag is like a real thing. It just didn't have a name before.

SPEAKER_01 59:46

It makes so much sense. It's it's that's amazing perspective. Wow, that just makes too much sense. Sorry. Um well, we'll start to kind of wrap this thing up. I'd like to um I'd like to just hear your thoughts on on the future, especially of wearables. I think I think we've got some real opportunities here to take wearable technology, and it's already happening, but I I think it's gonna take off soon. Where do you see wearables kind of making a an impact in the fire service?

SPEAKER_00 1:00:17

Well, it needs to be done carefully because I think there's, you know, this push for noisy data is is making decisions on it, is it it makes me a little nervous. Once you crack one of these things open and know what's inside of it and you know what it has the capacity to measure, I'm worried that we'll do it without human context. I'm worried that, you know, there will be automated systems that are going to make decisions and then they're gonna put out some fancy infographic that just says this is the way it has to be because this is what you know the AI data center says our AI overlords say that it needs to be. And I think the most important thing I can recommend is like, man, we have to have people internally who are willing to give, do the education and give these this information context because it there's there's going to be so much data, so much hypergranular data that's coming out that it's almost impossible to be like, oh, well, that just doesn't feel right, or that's not legit because now we have all this other information. So we need to prioritize the education internally and bring up some of these younger folks who are native to this language, to this digital world. And we need to educate them formally and we need to give them the, again, the street cred within the department to say, this is cool and all, but if we're not doing this without human context, if we're not adding humanity and lived experience to this discussion, then what are we even doing here? That's where I see the next important kind of horizon with this information is uh it's it's numbers without context will lead to bad decisions that uh, you know, without humanity really. And I think we're at that point as a society as well. But I think what we do have agency over is how the fire department responds because we're still irreplaceable. We're still humans responding to other humans' needs on their worst days. And if we don't reframe it that way, I think it's easy to get uh, you know, to get inundated with the avalanche of numbers that are soon going to be available. That said, I think there's also an amazing opportunity from an exposure tracking perspective, you know, what um what IPSDI is doing, um, what NERIS is going to be capable of. I know on their roadmap, they're talking about having wearable integration. I think about from, you know, um, like I said, exposure tracking for work comp claims for things like that. Like, yeah, there's going to be more availability, availability for that information, but when you talk about a career's level of exposure and you look at somebody who gets sick, it's going to be a whole lot easier to be like, all right, well, I have this carbon copy, you know, report from a guy who wrote it in 1996 from this fire I went on. Um, you know, I think there's there's a lot of promise there. I also think from the machine learning perspective, we're going to be able to train some of these networks to make predictions on the events that cause either medical errors or accidents or on-duty injuries, and then we'll be able to reverse engineer those out. So I think there's like, there's a ton of opportunity there, but it's all useless without human context. And I don't want, you know, you there's a study that came out from MIT of people who use uh use ChatGPT or Claude or whatever to write their correspondence. And like the way that the brain is no longer lighting up when you're using it as a crutch and you're not scaffolding your own, your own thought, like that's where I worry about like you can make a really cool polished email, but it doesn't mean crap to anybody if there's not a human behind it. So I would just say, like, uh as the world changes and the technology continues to increase whether we want it to or not, I think being able to maintain humanity and all of it is going to be critical to the way that we approach everything we do for the next hundred years.

SPEAKER_01 1:04:17

Man, so so well said. I've been thinking about it the same way. And we are so dang reactionary. We have to think about this. I mean, it we're we're what uh just a few years in to to to AI, to to even a chat GPT when when ChatGPT hit the hit the forefront like three years ago, four years ago?

SPEAKER_00 1:04:37

Yeah, and we're already getting closer to singularity where we might no longer need, yeah, no longer need us to participate.

SPEAKER_01 1:04:44

And we can't fear monger about it, uh, because it it is happening. The internet happened as well, and people were freaked out about that. The printing press happened, people were freaked out about that. But no, this is big. And uh I I think the people that that are gonna be the the most successful moving forward are the ones that can interact with the technology. However, um just putting your claw up against you know my clod isn't gonna take us anywhere. So, how we can work with that technology, but do it safely with humanity, um, and and understand the context behind it because that's the one thing it doesn't know. It does not know the context. And uh I it's very, very well said, and um, it is impressive. I think we've got a lot of opportunities, but it also kind of scares the crap out of me a little bit to be.

SPEAKER_00 1:05:31

Yeah, ChatGPT doesn't need white monsters or incrustables to keep it running to bring this thing whole circle. It sure doesn't.

SPEAKER_01 1:05:39

Well, is there anything else that we missed uh that you want to highlight with the research or or just anything before we close out?

SPEAKER_00 1:05:46

No, I think we're gonna have so we've got a pipeline of I think four or five papers that are gonna be coming out over the next year or so. We're in the peer review process with the public library of science to try and get this open access, get it out as quick as we can. But also from a peer review perspective, I would just say like this process has given me so much more appreciation for the rigor of good science. There's so much out there that somebody can, you know, just spout off and like, oh, I did this or I did this, but never got peer reviewed. And then it ends up and it becomes gospel. And, you know, there is so much more to doing high quality science, but part of it is that it doesn't happen fast. And it can, you know, what's the old adage? It can be good, it can be complete, or it can be fast. And at most you can pick two of those. Um, that's kind of you know what I've learned in this whole process is how long it takes, how collaborative it is to get experts from different fields and domains to look at it and say, you know, give you feedback to make sure that the product we're putting out in these papers is as actionable and, you know, as real as it possibly can be to help people make that informed decision. So I would just say from a scientific literacy perspective, like I would just challenge people to hold themselves to a higher standard, to, you know, actually put thought, critical thought into, you know, what you're using as evidence for different things, because there's just the amount of junk that's able to come out is is at a breakneck speed as well. And I I just hope people's literacy and you know the the hope to for critical thinking won't go by the wayside in the it because of how easily accessible and easy it is to put out uh information out into the world. So that's the only thing I've learned. And if I can save somebody else a little bit of time, then I feel like I've done my part.

SPEAKER_01 1:07:40

Well, you've done more than your part, man. You've done some impressive work out there. And I know it wasn't just you, I know you had a team behind you, but but we're following what you're doing. We're gonna continue to watch it. Uh if anyone wants to catch up with you, is there a good a good place to do so? I know you're on LinkedIn. Uh any any way for people to get a hold of you?

SPEAKER_00 1:08:00

Yeah, just email me, mbinny at westmetrofire.org. Um, I'd be happy to send all the the different information that we've learned. Um, we've put some posters together for the American Public Health Uh Association uh that are easy to send out. Um, and that that can you know be the a good starting point for conversation. Um so yeah, if anybody wants to really ruin an afternoon by talking about biostats or wearables or things like that, I'm always up for it. So yeah, just the that email would be fine or send me a message on any of the platforms. And I'd be happy to uh, like I said, ruin your afternoon.

SPEAKER_01 1:08:38

Not at all. There's there are plenty of us nerds out there in the world that that can geek out on this stuff. So Mike Benny, thank you so much for your time. Thank you for the work you're doing. I appreciate you coming on today.

SPEAKER_00 1:08:50

Of course. Thank you for having me.

SPEAKER_01 1:08:54

So, as always, if you have future show ideas or feedback, please email opstalk.wfd at gmail.com and we'll see you next time.