This week we have the honor to interview again a group of sports executives to discuss the future of injury reduction and rehabilitation.
Devan McConnell, the High performance director at Utah Mammoth (NHL).
Raphaël Ravet, the Chief Commercial Officer of Myocene, a leading Belgium based sports technology company revolutionizing how muscle fatigue is measured and managed.
Frederick Donnell, the Head Football Athletic Trainer at the University of Nevada Las Vegas (UNLV / NCAA).
You can watch the video interview below by clicking on the Youtube link. You can also listen to the audio interview by clicking on the link at the top of the page:
Here are some of the best quotes of our conversation with Devan, Raphaël and Frederick:
Q1. From Data to Decisions
🔹 Frederick Donnell
“We can get all the data we want, but at the end of the day, the real challenge isn’t on the practitioner side—it’s getting buy-in from the athletes. We can lead a horse to water, but we can’t make them drink it. You can show a player that their recovery score is low or their sleep is poor, but that doesn’t guarantee they’ll change their behavior. So for us, the missing piece in translating data into injury reduction isn’t necessarily better technology—it’s actually getting athletes to engage with and act on the information we’re giving them.”
🔹 Devan McConnell
“I think we’ve reached a point where we can measure almost everything in isolation—force plate metrics, HRV, sleep, workload—but what we still haven’t figured out is how all of these variables interact together. One metric might be down while another is up, and the workload might be stable, so how do we actually interpret that collectively? The real challenge is building a system that connects all these data points into something meaningful, rather than just looking at them separately. That ability to understand how the pieces of the puzzle interact is what’s still missing when it comes to truly driving injury reduction.”
🔹 Raphaël Ravet
“Today, most technologies are very good at quantifying outputs and external load—we can measure movement and workload extremely well. But where we still have a gap is in understanding the physiological response to that load, the internal side. Many of the tools we use are still influenced by athlete perception, motivation, or emotion, which introduces bias. What we really need are fully objective markers that track internal load reliably, without being affected by those factors. Improving that aspect—getting closer to true physiological response—is where I see the biggest opportunity to better support injury risk reduction and rehabilitation.”
Q2. Individualization vs. Scalability
🔹 Frederick Donnell
“When you’re managing large rosters, it really comes down to trusting your staff and making sure everyone is aligned. There are very few opportunities to truly work one-on-one with an athlete, especially during rehab, so you have to rely on your team to execute the plan properly. That means constant communication, making sure everyone understands the goals for each athlete, and staying organized across a large group. There’s no textbook that teaches you how to build that kind of trust—it comes from experience and ongoing collaboration. At scale, individualization isn’t about doing everything yourself, it’s about building a system where your staff can deliver it consistently.”
🔹 Devan McConnell
“For us, it starts with building structured systems and processes. We begin with a broad program based on key physiological principles and then segment players into groups using their data. From there, it progressively becomes more individualized—factoring in injury history, preferences, and performance needs. Eventually, especially in rehab or return-to-play, it becomes extremely specific to the individual and their situation. So the balance comes from layering—starting broad for efficiency, and then filtering down into deeper levels of individualization where it matters most.”
🔹 Raphaël Ravet
“From a product development standpoint, we have to design tools that respect the reality practitioners face—limited time and large groups of athletes. That’s why we focused on creating a single, actionable metric that provides immediate insight. It’s not meant to replace decision-making, but to support it quickly and efficiently. When you’re managing many athletes, you don’t have time to interpret complex datasets, so the information has to be simple, reliable, and individualized. The goal is to make it easier for practitioners to act in real time, without adding complexity to their workflow.”
Q3. Return-to-Play: Art vs. Science
🔹 Frederick Donnell
“In the past, a lot of return-to-play decisions were based on observation and judgment, but that’s not something I’m comfortable relying on alone. The last thing I want is to look at how someone is moving and make a call without objective data, because if that athlete gets reinjured, the consequences are real. What we’ve done is build protocols using sports science—force plates, testing, and data tracking—so we’re covering every base. That allows us to go to coaches with confidence and say whether a player is ready or not. For me, the goal is to reduce guesswork as much as possible by grounding decisions in measurable data.”
🔹 Devan McConnell
“I see return-to-play as a true combination of art and science. Data and technology are incredibly valuable for identifying red flags, quantifying progress, and supporting decisions, but the experience and intuition of skilled practitioners are just as important. We have clinicians with decades of experience whose ability to assess athletes is exceptional, and combining that with objective data creates the best outcomes. If you rely only on one side, you’re missing something critical. The balance may shift depending on the situation, but both elements need to be present to make the most informed decisions possible.”
🔹 Raphaël Ravet
“One key gap in return-to-play has been the ability to assess fatigue resistance, especially in comparing an injured limb to a healthy one. It’s not enough to restore maximum strength—fatigue resistance is often still compromised after injury. By measuring this, we can identify imbalances that weren’t previously visible and better understand whether an athlete is truly ready. Technology won’t replace the expertise or intuition of practitioners, but it adds another important dimension to the decision-making process. It’s essentially a new box to tick, helping make return-to-play more complete and more objective.”
Q4. The Next 3–5 Years of Innovation
🔹 Devan McConnell
“We’re already collecting more data than ever before, but the real question is how we make sense of it. Right now, we still struggle to connect the dots between different physiological systems and performance indicators. I’m interested to see whether AI can help us process that complexity—whether it can identify patterns, relationships, or even new questions that we haven’t considered yet. The potential is there, but it will depend on how effectively it can turn large amounts of data into meaningful insights.”
🔹 Frederick Donnell
“As new technologies emerge, we’re going to keep getting more data, but the real challenge is how we use it in a practical way. For me, it comes down to shifting from being reactive to proactive. Instead of responding after an injury happens, we need to use data to identify issues early and intervene before they become problems. For example, if we see asymmetries or changes in movement patterns, we can act immediately and start a conversation with the athlete. That’s where technology becomes valuable—when it helps us prevent issues rather than just manage them after the fact.”
🔹 Raphaël Ravet
“AI has strong potential not only in injury risk reduction but also in optimizing performance. Beyond identifying risks, it could help predict when an athlete will reach their peak performance within a training cycle, allowing practitioners to better plan and periodize training. This shifts the focus from just avoiding injury to maximizing performance outcomes. In the coming years, the biggest impact will likely come from tools that can integrate data and provide predictive insights that are both actionable and reliable.”
Q5. Alignment Between Practitioners and Vendors
🔹 Frederick Donnell
“One of the biggest challenges with new technologies is time. If a tool requires 10 or 15 minutes per athlete, it’s just not realistic in our environment. We’re managing large numbers of athletes, and there’s very little opportunity for extended one-on-one sessions. That means tools need to be efficient and allow athletes to be somewhat self-sufficient, while we still oversee the process. If it doesn’t fit into our daily workflow, no matter how good it is, it’s not going to be used.”
🔹 Devan McConnell
“From a practitioner standpoint, time constraints and athlete motivation are major factors. Athletes have limited time and are focused primarily on performance and competition, so if a tool doesn’t clearly impact their ability to perform, it’s difficult to get consistent engagement. Even if we believe strongly in the value of a technology, adoption becomes a challenge if it doesn’t align with the athlete’s priorities or fit seamlessly into their routine. That’s something vendors really need to understand when designing solutions.”
🔹 Raphaël Ravet
“From the vendor side, it’s critical to design products that fit real-world conditions and workflows. Early on, we worked very closely with practitioners and early adopters to refine both the technology and the protocols around it. Having a scientifically valid tool isn’t enough—it has to be usable and actionable in daily practice. That’s why collaboration is essential. By putting the product directly in the hands of practitioners and learning from their feedback, we were able to build something that aligns with their needs and constraints.”
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