Reimagining Athlete Monitoring: Why It Was Time to Put This on Paper

After years working in sports medicine on fields, in clinics, with athletes at every level I kept returning to one simple question:

Are we really monitoring what matters?

Despite the explosion of wearable tech, tracking systems, and performance dashboards, I noticed a disconnect. We were collecting data, but often not the right data at the right time. We’d screen athletes at preseason, call them “fit” or “at risk,” then hope for the best. When injuries happened as they inevitably do we scrambled to explain them retroactively using models that didn’t reflect the complex, real-time demands athletes face.

That frustration is what inspired me to co-author the recent viewpoint: Reimagining Athlete Monitoring for True Indicative Injury Prevention. It’s not just a paper it’s the product of countless observations, conversations, and reflections from working on the ground in elite sport and day-to-day rehab settings.

From Binary Labels to Human Complexity

One of the biggest issues I saw? The over reliance on binary labels: injured or not injured. These categories might make data easier to manage, but they don’t reflect reality. Any coach or physio knows that athletes live in the grey zone managing niggles, stress, fatigue, and load fluctuations that don’t quite qualify as “injuries,” but still matter immensely.

We’re good at tracking performance as a spectrum. We adjust based on form, fatigue, even mood. But when it comes to health, we often default to black and white. This paper pushes back on that. It encourages us to see athlete health the same way we see performance: fluid, dynamic, and context-dependent.

The Power of Timing

Another key motivation was timing. Too many decisions are made based on outdated data screening tests done months before the moment of need. If you’re trying to make a return-to-play call or manage load across a congested schedule, you need relevant, real-time insight, not something pulled from last season’s wellness report.

We argue that real prevention comes from timely, actionable monitoring not just a list of risk factors. That means integrating health data into daily workflows, interpreting trends, and having the tools to adapt before breakdowns happen.

Rethinking “Prevention”

This piece also aims to reframe what we mean by prevention. It’s not about predicting who will get injured. It’s about creating systems that help athletes stay closer to their optimal capacity where small signals aren’t missed, and early interventions are possible. That’s where methods like latent Markov modeling and spectrum-based classification come into play. These tools aren’t just academic they have the potential to guide decisions in real sport environments.

Why Now?

Because we’re finally at a tipping point. The technology exists. The demand for individualization is growing. And more professionals are recognizing that injury prevention is less about control and more about understanding patterns over time.

This blog and this paper is my contribution to that shift. A step toward smarter, more athlete-centered systems that respect the complexity of the human body, the reality of performance environments, and the need for better decision-making tools.

If you work in sport, this conversation matters. Whether you’re a coach, clinician, researcher, or analyst, it’s time to challenge the old ways of thinking and embrace new models that reflect the true nature of athlete health.

Read the full article here (BMJ Open Sports & Exercise Medicine)