When Data Stops Helping: Escaping the Trap of Training Metrics

it becomes too much when you start chasing the numbers that the data is giving and it starts directing you versus you using it to direct your training. It's the moment where it stops becoming informative and you try to satisfy the data where I think it starts to become too much and not be valuable and you become dependent on it.

Hello and welcome to the Athletes Compass podcast where we navigate training, fitness and health for everyday athletes. Today we're diving into one of the biggest challenges facing endurance athletes today. Knowing when our GPS watches, power meters and training apps are helping us reach our goals and when they're actually holding us back. Joining us today is Molly Brewer, a University of Florida graduate student whose research on sports technology and collegiate coaching

has revealed the hidden psychological costs of our digital training tools and more importantly, how we can use technology strategically instead of becoming its prisoner. Molly, welcome to the Athletes Compass podcast.

Thank you for having me.

Your recent research found that technology becomes most valuable when it's aligned with coaching goals. For everyday endurance athletes listening, what's the difference between using technology as a tool versus becoming dependent on it?

Well, I think one of the key things is exactly what you said, using it as a tool and a support to guide your training and kind of guide you towards your goals, but not letting it take over. One of the biggest things that we've seen in our research is that it can be very valuable in providing feedback, helping execute training, staying in a prescribed intensity, looking at data streams like power and heart rate and using that to guide effort. It can

bring feedback of how to then make the next steps towards guiding your training. But it becomes too much when you start chasing the numbers that the data is giving and it starts directing you versus you using it to direct your training. It's the moment where it stops becoming informative and you try to satisfy the data where I think it starts to become too much and not be valuable and you become dependent on it.

⁓ so where it might be valuable or a lot of our athletes kind of describe it as getting you out of your head is when maybe you've spent the whole day at work or school and that's taken a lot of effort, lot of cognitive, kind of deposits of your bank account that you just have and you need to go out and do your workout. And sometimes just having the data to tell you, this is what I need to hit. This is what needs to guide it. It can reduce that cognitive burden. But then when you chase the numbers,

and you get anxiety over, have to satisfy this certain metric or this number out in my training. That's where it stops being valuable and then starts just adding, not reducing cognitive burden, but adding cognitive burden when you almost the data starts to judge you. Like I didn't do good enough because I didn't hit my numbers. Or when you're looking at your numbers so much that you don't even see the beautiful scenery. Like I lived in Colorado before.

Hmm.

and there's beautiful waterfalls and mountain ranges. You're so fixated on your number that you don't enjoy that part of training and riding.

Yeah, nice. Yeah. Well, I think it was well said you just have to, make that realization just like you said, like, look at the beauty around us. it really that big a deal that I, you know, I didn't hit my target wattage for that effort or whatever on the hill today? Look where I am. So yeah, so true.

Molly, you studied coaches across multiple sports in your research from football to volleyball. What patterns did you see in terms of when technology helped versus when it became a distraction or when chasing numbers became a problem?

From the coaches in our study, so we did look at multiple sports from women's volleyball to men's American football to men's basketball to women's soccer. So wide variety of sports and genders and collectively across those we saw that the technology was helping coaches enhance their decision making. That it was a multiple, it was multiple inputs for them. It wasn't dictating everything, it just.

allowed them to confirm, refine and challenge what they saw visually in their athletes and what they knew from coaching experience and from sport specific knowledge, like having this deep knowledge of their sport. And it didn't replace their coaching expertise. It was, it served as a tool to kind of enhance it. So that was like a value of the technology. And then I think something we can all agree with is that technology allows us to measure things we couldn't measure before.

It gives us insights into our bodies that we couldn't see or know. And I think when you're at the highest level of the sport, having that 1 % difference to improve performance and reduce injury, many of these coaches could now see very minute muscular imbalances, asymmetries in how their athletes move. Even the dietitian was like, nutrition is very aesthetic based. I look like I'm getting bigger. And now we have

ways to measure body composition. And these are really valuable across all the sports, like having this insight that we just never had before. But where they talked about it kind of becoming a distraction was when the athletes kind of became too fixated on the numbers that it affected their gameplay. So this was actually from one of the athletes and not one of the coaches. One of the examples was getting so fixated on their three point percentages in their statistics.

that they stopped taking three point shots in the game. And like it was the difference between like letting the numbers control how you played your sport because you were worried about what the outcomes would be. And then the other biggest thing was testing was becoming too frequent. Like to get the right numbers, like for in collegiate sports, a lot of times they'll use force plates. So the athletes have to jump on them before and after every single game and practice. And sometimes it's just so much, it would be equivalent to us having to do a 20 minutes.

Hmm.

FTP test all the time to get the accurate numbers and it was fatiguing and when you don't see value in that amount of testing, you lack the quality needed to get good data. And so the athletes didn't put as much engagement and then that affected the data quality and the decision making along the line. So those were kind of the big examples of where data got to be too much that it actually affected decision making and play.

That's super cool. As a coach, I can see the benefits and I feel I'm also an athlete. I feel like the push and pull. As a coach, somebody who is new, data can be so beneficial to, as you said earlier, to do the workouts correctly at the right intensity that the coach has prescribed.

the constant data overload and like focusing too much on the data can be so mentally fatiguing for the athlete. like we've talked about having a off season. Sometimes I feel like I need to take a step back and just turn. Like I've started to run without watch because I feel like I'm at the point where I need.

deload the mental load of having the data there all the time. And then I start not seeing the value of actually enjoying my run and just the feel and the rhythm of running. And I'm just like looking at the pace, right? Where am I? So sometimes I feel like there's that push and pull between the athlete and the coach.

Totally. Yeah, would just totally agree. I feel the push and pull as well. I just onboarded a new athlete, high level triathlete coaching. And he had all of this, basically this history on his Garmin with his HRV already kind of collected. And in a very quick period of time, onboarding to Athletica, full recovery profile chart there.

only working with him for days and he happened to be in the tank and he said he was, had no idea this technology actually existed. And here his whole HRV profile was to the level of a Dan Plews sports scientist. It was fully analyzed and whatnot. And it was also flagging at the same time and mirroring how he felt. And was such an easy decision, right? So to Molly's first point.

It's incredible what is just now available for us. But at the same time, feel the, can be too reliant on it too. It's such a funny, funny time with the whole push pull thing. And yeah, where, where do you find the balance, Molly?

think we're constantly still learning. mean, the development of the new sensors and technology, we're in the new frontier of it. And I'm kind of exploring it in my research, but I think there's so many more experiences out there to hear as we kind of develop. And I think there's a need to develop data literacy among athletes. So that's understanding what weight to put into a metric, how to understand day-to-day fluctuations, how to interpret trends or when something might be off. And we...

Hmm.

teach that in computer science about how to spot misleading numbers or where the data might not be perfectly clean. But I think we almost needed to teach that in our relationship with data as we move forward of maybe not even confined to sports, but kind of everywhere in life of like, how do we live with data and develop these strategies and resilience to help it serve us, but not have it direct our lives?

so I think just encouraging, like whether that's a coach teaching those skillsets, are you developing it through trial and error, like reflecting on your data? And we can talk about this more of like, how do you might do that? but I don't think there's a clear answer right now exactly how to balance it. And I think it also changes with us. Like who we were yesterday may not be the same we are today. And that relationship with data can change across a season, across your time. Like I was very much into data.

Mm-hmm.

until I got into my PhD and now I have to focus my time on a lot of other things. So sometimes I just go out and ride now because it doesn't direct my training as much as it may have done prior to the PhD and that's okay. It's just kind of how our lives change and how we influence it and integrate it.

Yeah, that's beautiful. agree with the educational compound. We need to continuously educate ourselves and like Athletica users as well. Like what does it mean?

Let's talk about techno stress.

Yeah, you guys sent

that to me and I had not heard the term before and I think it's really fun. So I hope that I kind of relate it correctly, but I'm guessing it's related to kind of this distress or negative feeling regarding tech use. that? Yeah. Okay. And I think the biggest thing like we're talking about here and I want to point out is technology is really cool and really useful and it can kind of de-stress a person too.

really?

Yeah. Yeah.

but it can also contribute to stress. in a research that I just did, we had the most salient terms used by collegiate student athletes regarding their data was it can get me out of my head and it can get me in my head. And this was related to the same data stream. And in one sense, it reduced that cognitive burden. It validated what they were feeling inside where they like, sometimes you have to decide, am I pushing enough?

Hmm

Should I do this workout? Like they're very competitive. They have goals and the data kind of validated like, Hey, you know what you're feeling in your body? I see it in the data too. I think that's kind of what you were talking about with your athlete with the HRV. and that, you know, kind of can reduce stress, but the techno stress you're talking about is that we have a lot more data than we've ever had before. Like what, like recreational.

Totally. You got it.

athletes can get power meters on entry level bikes. Like everybody has GPS enabled computers. And I did an interview with recreational cyclists and every single one of them was uploading their data to a platform like Strava or Garmin Connect or Interval ICU or Athletica. And so like we have this access for recreational all the way up to competitive context. So we have what we've never had before in terms of data and data overload.

So I think it's like being careful in those sense of, especially the social media component of data use. think that's where a lot of techno stress comes up is this idea that like, you've got to post your data to, for other people to see. And so I think that's a component of it. It's not just like your own data, but how other people interpret your data. So not how you interpret it, but how the world's interpreting your data can add stress. But yeah, I definitely see techno stress.

Mm-hmm.

as a cool new word that we're going to be using a lot in the future.

Yeah,

I think it's fun too. One of the stressing points that you mentioned, when you have your Strava data there, anyone can go in and look at your average power or pace and all that. It can kind of create, you're more conscious of your digital social media footprint as an athlete.

Mm-hmm.

or you're just thinking about what you will call this workout after, or there's so many little rabbit holes that we can get into there. But I think that also the techno stress could be the uncertainty what things mean. We have so many definitions.

that we're measuring one set of data, like we people can't interpret what it means and what it means for them. So some of that techno stress is just like, we don't understand the data. What is it actually telling me and in my life context.

I agree with that. And I think my background is also as a sports scientist. So one of my biggest interests related to probably this techno stress or how we leverage data is how sometimes it pulls us away from foundational sports science principles. So we all know that you need to balance rest and training. You need enough stress so you can get better, but you need rest so that your body can rejuvenate. And that's where like

you gain a lot of the fitness that was in the rest. Cause if you do too much, you can risk injury, illness, overtraining, but a lot of our technology kind of has this sense of always getting better. like Garmin's new VO2 max, number, it people I've heard just around that people were chasing it to try to get better and better VO2 max numbers. do we have?

Maybe that's Paul.

Prof I'm

just looking at you, Prof.

You

What

I was doing in the beginning too, but the reality is that as you get older, some of these visualizations aren't designed for everybody in their context. Like we know that VO2 max actually decreases as you get older and some of the goals is maintaining it. But we have no visualizations that encourage people like, Hey, good job at maintaining your VO2 ⁓ instead of like, or like when it starts to decline, it can be like, it can turn people away from.

from data when it doesn't match their context or like sports science principles, or we have leaderboards where we know staying on top of the leaderboard every single week does not align with sports science principles that you need a down week sometimes. So that's one of my biggest things is how technology and the visualizations might pull people astray from sports science principles.

Exactly.

Yeah, I just want

to say something on that because my power numbers were basically the same as last year. But again, to your point, Molly, I think the algorithms in the Garmin VO2Max must have changed because despite those same power numbers, my VO2Max has gone down in Garmin. ⁓ yeah, I was...

you

Mine has plummeted, so don't feel too bad about it. But to your point, VO2 max algorithms, this summer I went to Canada where there's a lot more hills than in Texas. Of course, because you're running uphill, you run slower. My VO2 max on the Garmin just plummeted. You know, as a sports scientist, that is not what happens. I'm getting stronger.

OK. ⁓

No.

The algorithms don't take that into account that I was climbing up the hills like never before.

Author Matt Fitzgerald, who told us in episode 41 that athletes who need their devices the most for pacing are actually held back from developing true pacing mastery. How does this connect to your findings about the coach athlete relationship and technology?

So this one was a really interesting question for me because I've actually read and seen the opposite in my work. And that was that we've seen that access to these data streams, specifically heart rate, that's like the most well-known to be used across the sports, actually gives athletes a sense of their effort. Like it helps them connect with their bodily sensations. So they were able to see like this corresponds to a certain intensity level.

and it brought more learning about themselves that they didn't have previously and that they were actually overshoot or undershoot without kind of the heart rate to give them that learning. And we found in the research that there's really a triangulation happening and it's not one or the other. It's kind of a learning that's happening where you ask your embodied knowledge of like, do my legs feel? How do I feel today? What does this?

Data say like, what did I do yesterday? Like, what should I do today? And then the third kind of triangle of that is what does my life allow? Like, what can I do today? And those are the three kind of things that triangulate together to determine training. And I think you're asking specifically about pacing, but we have seen that having power and heart rate and some of these metrics available.

Hmm.

actually helps athletes develop a learning to pace better. But I think once you get up to the higher levels, especially in competition, like we've seen that in competition, the data streams are removed and you go based on your like self perceived pacing so that you can race your bike or compete in your sport. But in training that data and that information actually assists the athlete in their pacing correctly.

Yeah, I love that. I love that Molly. That's just exactly what we promote and try to teach our athletes. That's how the tools of technology are supposed to be used. And I love the term triangulation as well, right? okay, here's the prescription. Here's the heart rate target. Here's the pace or power target, right? Here's your external load. Here's your internal load. For us, sports scientists, that's what they are. But it's like, here's a...

Yeah.

Mm-hmm.

Here's a tool that's telling you this. Now go out there and try to be within these targets and see how that feels and do exactly like you described. It's like, I almost kind of play a game, do a little bit of a guess. I wonder what my power is right now. I wonder what my pace is or I wonder what my heart rate is and have a guess. And how are you doing in terms of guessing where that should be relative to the objective of the session? And that is, and then you...

you should almost get to the point eventually where you almost don't need the tools. The tools were there to, and the prescription was there from your coach or AI coach to help you, help guide you towards your goals. And then how are you going in terms of developing your own mastery at that objective? So that's sweet.

No, I think I said exactly that as well.

Yeah.

Yeah, and using the tools to teach yourself, you know, the pace, their internal intensity, and then you just throw it away for the race. Like oftentimes you see these high level athletes, they don't even run their race with the watch or they take it out. Like triathletes take it off and just like run by feel, but you need to get to that point where you can go by feel.

And oftentimes we get a little bit of criticism like what do you mean go by feel? Right? I don't understand this feel thing. Like what do you mean? Like what pace should I go?

Yeah, we do.

you

I always look at it as I heard one time someone say, if the escalators are broken, you can no longer go up the stairs when really you can. and so I think about that when I decided to do a workout that I never want to say, I can't ride my bike without my power meter, heart rate monitor, even though I am somebody that loves them and turns on that data stream every time I ride. But I want to know that if I needed to go out and ride my bike or race my bike without it, that I could do it.

Mm-hmm. Yeah.

Yeah. You don't want to be sitting at 250 Watts in a certain heart rate and then watch the race go up the road because you are saying, I can't go above 250 Watts, you know, and then the race is gone. And then you are stuck. You've got to learn to ride by feel or run.

Marjanna?

I actually

did my first Ironman that way. I had set my data stream so tight that I didn't know how to listen to my body. like, just trust the data, trust the data. And I was wondering why everybody passed me on the bike. That was my slowest bike ride, by the way.

Where did I?

Well, I'm dating myself,

but that's how we, yeah, that's how we used to go, right? We used to run and ride without any of the technology tools. That's just how you go.

Yes.

Yeah,

but in my case, my power meter wasn't correctly installed. So it was showing me wrong power. So I learned.

I see. I see. Got

⁓

it.

Your work mentions how technology should support individualized athlete management. For endurance sports where athletes often train solo, how do we balance self-monitoring with over-reliance on data?

This is a really good question too. And I think I'll add a little bit of context, which we've already talked about here is that my work is in collegiate athletics, which is generally team sports. So you're under the assumption that a lot of times practices are going to be structured the same way for all the athletes. Like you're going to run through the same drills and practice structure. And we found that technology really helps the coaches individualize their programs, even among this team structure. So they can look at.

Mmm.

data or maybe an anomaly in the data and cut reps for certain athletes or give athletes more and kind of offer this really individualized assessment within a team structure. And that's important because every athlete reacts and adapts differently to a given training load. And so I think the important takeaway I was thinking in terms of the endurance athlete is the way you adapt to your training will be different from someone else you see on social media.

So what someone else is doing in their training may not align with what you're doing. And so you can really leverage these data streams and these platforms to offer that individualized monitoring for you and try not to worry too much about what you see everybody else doing.

Yeah. No, it's amazing. And I think that's really the Holy grail within team sports for sure. And, you know, it's, I know we're, not fully there for sure, but, but it's like the, future is, yeah, is bright with respect to that and being able to individualize the training for each person that is on that team. know, teams have up to, you know, 20 or 40 individuals, getting them all.

Yeah.

Optimized in terms of their their training that we do this of course in Enough in the Athletica context in velocity MJ is able to do this on ⁓ velocity is our live interval training Platform so basically you can do an Athletica session there like a 30-30 and then you can actually watch like every individual that's doing that MJ's coaching it live and she picks out she's ⁓

can actually picks out the power and heart rate response on for each person that is on the call. And she can like almost like the, the fact that she can live coach every person there remotely is, you know, is, amazing. And she does. I think the last podcast that we did, we were giving the example where there was an athlete with that had a heart rate that was just soaring and skyrocketing. she.

She just, you know, she asked that athlete to just hold on, just let's calm down on this next set, maybe just take a couple off. So again, the tech sphere is incredible from this regard now.

Yeah, for really just diving into what you need for your body and your training. It's pretty incredible. If we respect it.

Mm-hmm.

And that kind of leads into our next question. mean, that, you you found in your research that technology offers, support for decision-making when properly integrated, for our, not but, and for our podcast listeners who use GPS watches and power meters and training apps, what's your framework to respect that process to trust the device versus trusting internal sensors?

I think it goes back to that triangulation, always kind of triangulating them together. It's not one or the other, kind of talking to yourself about how, what is the day to show versus what my body is feeling that day, and then also the what does my life allow me to do today? And really keeping all of those ideas in the forefront as you utilize technology. But in terms of like, what would be kind of

my framework or for advice is this is also a very common computer science term and you guys may have heard it but it's garbage in garbage out. So what you put into the device is what you get out of the device. So I do think there is a time effort trade-off to setting up your devices and platforms correctly so that you get the output correctly like that you're using it because it would be terrible if you were

using all these insights and they were dictating your training and causing this techno stress and it wasn't even set up properly for you. And so I, I do think that you'd need the time to kind of like set up or some of these devices rely, rely on a calibration period. Like it needs time to learn you and have a baseline time to understand like what your metrics look like so that when it detects anomalies, they reflect what your baseline is.

And so understanding that maybe if you put it on right out of the box or you use it right out of the box, it hasn't had that time to learn you and it hasn't had any inputs that reflect your body. So you have to take that as a grain of salt and how you use it in your training thereafter.

Yeah, big time. So kind of going back to the example I mentioned with the athlete that I just onboarded with Athletica coaching him now. And, this is, yeah, it's a, it's a super important issue across all platforms, but for ours, we have like a two year backlog of kind of data. So this, in this, in this case, the, the athlete had already used his Garmin watch for, ⁓ I'm not sure how long, but it was a long, long enough time where I just had these, you know, historic

baselines and then Athletica had these historic baselines and we could already get like, so in the heart rate variability example, we already had 60 days of normalized heart rate variability data and we could actually see when that seven day rolling average rolled above or below and of course it was below the 60 day norm. So yeah, but you're right, Molly, that you've got to have that historical data and sometimes that can take time to...

to develop if you're just buying a brand new device.

So that would be my my first biggest like trust is you know trust that the inputs in are going incorrectly so you can trust the outputs but then the second would definitely be the trying to triangulate how you feel so you determine your own self calibration and I think that was kind of what you were saying about your first Ironman where your power meter was off and it took a while to like calibrate that to be like you could actually go faster because your internal sense was

saying like, this kind of seems slower than what I'm used to. And that kind of goes back to that data literacy we talked about of like being able to make the decision or knowledge of when something is off. And that takes time. takes interacting with these technologies and not giving up on them ⁓ right away, but also not letting them dictate and kind of like use your own internal sense to form your own calibration with these devices.

Thank

Yeah, totally. So good. Looking ahead with AI and more sophisticated tracking coming, what should endurance athletes be thinking about now to avoid becoming overwhelmed by future technologies?

This was also a good question. I think we should be excited. I love technology and I think it's very useful and I think we've talked about all the benefits it can bring to us. But I think not being overwhelmed by it is letting it not dictate you. So again, like we said, like having this maturity and resilience and how we integrate technology to our lives. And I think being in computer science with

the AI, I'm not scared of it. think it's going to bring some really great benefits to both sport and life. But just remember kind of what we just talked about is you need to think about what's going into it. So we are continuing to get better and better. But if the data set that it's building off of does not reflect your demographics, you need to consider that when you are taking insights from it. So if you're from a minority or a female,

or maybe not a demographic that's strongly represented in the AI, the output may not reflect exactly what you feel in your data. And so I think we need to be conscientious about that as we make our decisions. But I'm encouraged that it is constantly getting better.

Hmm.

Molly, talk about your initiative where it's yourself and I believe, was it Ingrid as well from Norway, who's really looking at trying to get more women involved with tech to kind of come together. Tell us a little bit about that.

Yes, so Ingrid, Danny and I met in a spring break course with Dr. Stephen Seiler. And it was a sports science and technology course. And I saw it and definitely wanted to be involved and happened to be over my spring break, but I had to get up at 3 a.m. every morning to participate. But I got to meet Ingrid and Danny, who are two females also involved in their governing bodies. So I have done some work for USA ⁓ Cycling.

you

Ingrid works for the Norwegian Olympic Center and Dani works for rowing. And so we both have our foot in the door in sport. But we also are getting, well, Dani's completing her masters, but Ingrid and I are completing our PhDs and they all involve sport and AI and tech. And to find three women that were kind of balancing all of this was incredible because we're typically very few females in the room and in sport. I know in cycling, like there's not a ton of...

females who work in sports science either. So it's nice to share that experience and then elevate each other and then others that want to join. And kind of this initiative, yes, anybody that wants to join, we're like open doors, anybody in that space, because we need more presence there. And we need support too, in that space for each other. I don't think it's an easy

an easy field. I I came from sports science, professional cycling, and then I decided to go to computer science, which is also notoriously male dominated. So in every, every aspect of my career, I've been the minority kind of in those fields.

it's an awesome initiative and it is an underserved demographic, right? ⁓ It's just sport for better or worse, it just tends to be more male dominant, but we've got 50 % of the population is female obviously, so why we just need a greater representation there. ⁓ MJ is very passionate about leading that.

So I know she's keen to, we're keen to have more women using Athletica and then more females as well that are supporting in our tech. know, contribution and understanding and optimization of the whole process of being healthier and training and performance. We just love it all. Everyone deserves to be involved.

Yeah, when I saw that post on LinkedIn, like, how do I join? Like, want to be part of this. This sounds so much fun. like, I agree with all your points, like even sports science, like we do have female sports scientists, but we're very spread out over the world and in different sports and we are a minority. And then you add a tech component in it and

I love that.

I see tech and AI specifically like so useful for us because we are.

underrepresented in the research and like it just enhances our understanding of the individual differences that we face.

Yeah. I guess the, one of the things that we're developing in Athletica is really around the use of agents, Molly, that you'll be familiar with, I'm sure, right? So Andrea Zignoli and Steph, Stefano Andriolo they really are working hard on an agent-based system within Athletica. And one of the coolest things about that

And so for those, to back the truck up and explain agents, so agents basically were, they're working almost independently. We call this an agentic framework. it's basically, these little AI bots can kind of get to work and do work on a project if your infrastructure is sort of set up appropriately as ours is.

The coolest thing about this is that you can start to see how things are, one thing is related to another. We've always been challenged this way, Like heart rate variability I've been talking about, that's one example of, in the past we've just had to have like a specialist that's just involved in that. And then you see how that heart rate variability might be related to some of these power and heart rate measures and all these things that Molly's been talking about.

But here's another one where let's bring in the female menstrual cycle, right? And the various different symptoms that might be individualized that MJ's talked about at length on the podcast before. And now we start to get to the position where we could start to put two and two and three together to start getting even deeper insight and intelligence. And maybe you want to riff off that, on any thoughts.

think it's all very exciting. I'm not afraid of like where tech's going and how it's going to benefit our sport in people's lives and uncover insights that are individualized, all the things we've touched on, the value of technology. Yeah, I think it's exciting to hear that we're going to bring something like that into a sports platform. I'm very excited about that.

It's like our own female physiology sports scientist minion working for us.

that everyone gets access to, right? It's democratized around for every individual.

Yeah.

Yeah.

I think that's something I think is really important. So I'm in the field of human computer interaction. So it's about how humans interact with technology and computers. And one of the big founding philosophies of the field is that this interaction should be for everyone. We should never make visualizations or technology that only one subset of the population can utilize or leverage. That we should make it so that everybody has access.

to the types of insights and technology and understanding that this technology can bring, like the benefits of it. So I think that's kind of like what you're doing is like to bring these benefits to a population that may not have those insights. And I think that's like a foundational pursuit that I love to hear.

You know, Molly, for our Athletes Compass community, you know, what's a concrete step that you would suggest that they could take to ensure they're using technology as a supplement to their natural abilities rather than a crutch?

⁓ I think it's really thinking about developing those data literacy skills. And that happens after every session of like, okay, let's pick a session. Maybe this week, your athletes decide to do an integral session. They want their power and heart rate to guide their target. So they accomplish the workout, but then afterwards, they might want to take a moment to reflect on how they felt in that workout and to ask questions of like,

Did those numbers match with the sense of effort that I felt? Was I pushing? Was I holding back? Was I right on target? But then also, how did that compare to previously how I did those intervals? And then just build that literacy as you do workouts. But you can also flip it and then have maybe it's not a targeted interval session. Maybe it's an easy ride or run. And this time you just want the data recording in the background.

And you let your effort guide, but then you can check in it later and say, did it match up with what I was thinking? And then you can kind of build this, literacy around your data to the point where maybe not every session requires this much thinking. know sometimes when my device is like, rate this one to 10 on how you felt. like, I don't want to think that hard right now. I just did my work out. But I think it does build that literacy. We really need to integrate data.

You

to try to pick out when it's wrong and when it's right and when we might need to change things or update just so that the data doesn't drive us. We drive the use of the data.

Molly, ⁓ we're big on using, leveraging the large language models within Athletica to gain further insight into the data. How are you leveraging large language models in your own practice and then back to the user or just back to the individual listening, how do you think they should be leveraging that technology?

Great question. I think one of the benefits of large language models is that it can help you decide kind of what to do next or how to create alternatives. So before we had to rely on kind of coaches to decide like, oh, it's raining today. I have to change my workout. Now you can kind of go to the LLM and say like, okay, give me three options because I can't do my workout today because it's raining and I can't ride.

kind of get that feedback and it becomes kind of like a co-pilot in your decision making, which I think is really cool. It's like brought something that was maybe only available to those that could afford coaching down to like a lower population that can still like access that type of insight. I also think it can be helpful to decide, I don't know, like,

nutritional strategies, kind like you can you can chat back and forth with it and make a race plan and kind of tell it like, I mean, this is not related to like race plan nutrition. But if I have ingredients in my fridge, I can ask it to create a recipe out of it. That's maybe nutritional. So I think there's a lot of it with like this conversational person that can bounce ideas off of.

Hmm.

Yes, And my wife appreciates it. I've gotten better.

Yeah.

⁓

so that's kind of how I use it is like, I have an idea. Let's see what it thinks about my idea. ⁓ and give me a couple of different options. And I think that's where it is. can be very beneficial. It's like helping you decide next steps and having the different options to decide those next steps on.

Hmm.

I struggle to read fine print, so whenever I get a new gadget and I can't read the fine print, I take a picture and ask ChatGPT to translate it for me and give me some examples of how I can use it.

I bet you could also, that made me think about, I bet you could also like help it, let it help you utilize your technology better. So maybe if there's a metric in Garmin, you could be like, how is Garmin deriving this VO2 max metric? Cause I have done that before and I know that you have to be outside. If you run inside on a treadmill, it'll lower your, your, uh, your VO2 max. And I didn't know that until I asked, chat, chat, can, so.

Excel.

Yes.

Yeah,

yeah, totally. Good, good, good example.

Same on the bike.

I'm experimenting with one of their new watches right now and so was trying to figure out like why it affected my VO2 max and one of them is like the economy metric goes into VO2 max and you have to be outside to get that metric correct so if you spend your whole winter inside I'm not sure for the bike but for the run it'll lower your VO2 max.

Wow. Molly, is there anything more that we missed or need to address for our everyday athletes?

So.

I think we've touched on a lot of really good points. I would love to hear more experiences from everybody because I am not an expert in this field yet. Like I said, I think we're on the new frontier and I'm learning something new every single day. And like I said, every single person's experience matters. hearing more and more people's experience with technology, what they find beneficial, what's challenging for them. I'm always open to hearing new things.

That's awesome. That actually segues perfect to my last question. What are you doing for your PhD? What's the next study that you're doing or how are you wrapping things up in the PhD that you're working on?

Hmm.

so my next study is actually trying to blend my master's degree with my current PhD. So I was in sports science prior to this, and I did a lot of lactate testing and physiological assessments. And I'm curious in how people leverage insights from physiological testing with their technology and devices and our devices getting so good that we may not need those physiological assessments.

⁓ and I don't have the answers yet. know that I have coaches reach out to me because they, they do still want those assessments, even with the advent of all the new technologies we have. And I kind of want to understand why, like what benefit, like, is it having the right threshold numbers and that input that makes the output better? Is it a trust that like, this is actually my data and they don't trust that like data from the watches is their data yet? I don't know. These are just things I want to explore.

kind of bridging the two together.

Wow, love it. Well, Molly, just I'll just say it openly. We do have a research based platform, the sports science base. So if there's any ever an opportunity to to leverage the Athletica platform in some of your studies, please, please ping me a message.

Yeah.

Yeah, we should definitely chat. I love what you guys are doing. It's very exciting. Plus, I feel connected because kind of your partnership started at the Science and Cycling Conference, and that was kind of where I got connected to research as well. So I think it's pretty cool.

It's a great conference, that's so cool. Yeah, very good. Where did you, did you present there and things blew up from there?

No, I didn't get that opportunity yet, but because my background was in sports science and I worked in professional cycling, it was where I really got connected to the research side. I never actually thought I would end up in a PhD, but kind of like found my way there and that's what really inspired me to like learn the research side.

Didn't know that, that's cool.

Were you an athlete,

coach, a sports scientist? What were you doing in a pro cycling team?

Yeah, I worked more on like the sports scientist and logistics side. I worked for a Nigo Sanmulán for my master's degree. So I got to work under him with some of the professional teams he contracted with. But then after COVID, I went on to work in women's cycling. But

Wow.

I worked mainly on the logistics side. I think you wear a bunch of hats in women's cycling, even if you're there to help with sports science. If they need you to stand on the side of the road in the rain with wheels and water at Perry Roubaix, that's what you do. And I was very, very happy to do it and support the riders in whatever way they needed on those particular days. But that taught me that, you know, sports science operates outside of vacuum, outside the lab. And these are lot of the contexts you need to consider when we're developing technology or sports science.

methodologies. So yeah, I got to work in professional cycling, which was incredible. And I was presenting all my work with professional cycling at a conference and my now advisor was in the audience and invited me to join her lab in computer science at University of Florida. So that's kind of the connection of the story.

Wow.

Wow, that's amazing. What a great story. And again, I think the lesson is you never know, like you went to that conference, you never know what's gonna come out of it, right? And look at the road that you were able to kind of turn right onto that road, right? And go down in that pathway, so cool.

And just,

and I feel like in this new field, I'm really representing the voices of the coaches and the athletes and the people that are like on the ground trying to utilize these technologies. And I'm helping hopefully make the future better. And we understand all these dynamics that we're talking about, so we can continue to build really cool platforms and technology, but also help people through some of the maybe downsides of it or.

navigate some of the concerns we've brought up on the podcast today.

I am so excited to learn a lot more from you and the others on the Women in Sports Tech Club.

We can't wait to have you. If you haven't talked to

me yet, I'll make sure you get connected.

Yeah, she got me connected, so thank you. I'm super excited to really learn from you guys and push this whole field forward.

Molly, thank you so much for joining us today.

Yeah, thank you for having me. This has been great. I've loved connecting with all of you.

⁓ a good conversation. Thanks for listening today to the Athletes Compass podcast. Take a moment now, subscribe, share, and let's keep navigating this endurance adventure together. Improve your training with the science-based training platform, Athletica, and join the conversation at the Athletica Forum. For the future, Dr. Molly Brewer, Mariana Rakai, and Dr. Paul Laursen I'm Paul Warloski and this has been the Athletes Compass podcast. Thank you so much for listening.

It's been awesome. You've been great, Molly.

Thank you.