Reimagining athlete monitoring for true indicative injury prevention

Abstract

Injury prevention and performance optimisation in sports are evolving from generic strategies to more personalised, timely and data-informed approaches. Traditional systems often rely on infrequent assessments and binary classifications of injury, which fail to reflect the complexity of athlete health. This perspective advocates for a refined approach to athlete screening and monitoring that captures historical and real-time data to support precise, context-driven decisions. We differentiate between screening as a point-in-time evaluation of underlying risk and monitoring as the continuous assessment of an athlete’s changing condition. The key principles of this approach include using valid, outcome-specific measures, timely data collection close to decision points and seamless integration into daily practice. By moving beyond simplistic metrics and incorporating advanced analytical techniques, this approach offers a more nuanced and proactive way to manage athlete health. It bridges the gap between research and real-world application, supporting better decision-making and long-term performance outcomes.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Sports injury prevention has increasingly shifted from generic, group-based programmes to more personalised approaches that target individual risk factors—also referred to as indicative prevention. While promising, these strategies are often hindered by outdated screening methods, binary health classifications and a lack of real-time, actionable insights.

WHAT THIS STUDY ADDS

• This viewpoint presents a refined approach to athlete screening and monitoring that emphasises timely, context-specific data collection, spectrum-based health assessment and integration into daily decision-making. It proposes a clearer distinction between screening and monitoring, advocating for more nuanced and proactive strategies.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• This approach may shift the focus of athlete health management from static, retrospective assessments to dynamic, forward-looking systems. It encourages research into more accurate and responsive tools, supports clinical practices that address early health indicators and informs policies that promote scalable, real-world injury prevention strategies.

From universal to tailored approaches: the state of injury prevention

Injuries remain challenging in sports, affecting athletes’ performance, careers and overall well-being. Over time, prevention strategies have shifted from generic, one-size-fits-all programmes aimed at groups of athletes (ie, universal prevention) to more tailored approaches that address an individual athlete’s risk factors (ie, indicative prevention).¹ These tailored strategies were once confined to elite sports due to the required resources. However, advancements in wearable technology and accessible monitoring tools have made them more widely available.^{2 3} This shift is a significant step forward, bringing personalised injury prevention to athletes at every level.

Our current use of indicative prevention still has some major gaps.⁴ Many screening and monitoring systems today lack the precision to capture subtle risk factors and key health metrics, which are crucial for timely interventions. In this regard, we define screening as the process of systematically evaluating an athlete’s health status at a given point in time to identify existing vulnerabilities or risk factors and monitoring as the ongoing collection and interpretation of data to track changes in an athlete’s condition over time and inform timely decision-making.⁵

To unlock the full potential of indicative prevention, we need to rethink how we assess and manage injury risks.^{6 7} This viewpoint takes a closer look at these issues. We will discuss the core principles of indicative prevention, examine the shortcomings of current practices and propose an approach that supports more effective data-informed injury prevention strategies.

Walking the tightrope: balancing performance and health

Athlete performance and health go hand in hand, depending on a delicate balance between an athlete’s training load and ability to handle it (figure 1).⁸ Training aims to boost performance by gradually increasing the load, which triggers adaptations and builds the athlete’s capacity.⁹ But maintaining that balance can feel like walking a tightrope—small missteps in either direction can lead to problems. Pushing too hard or fast can lead to fatigue, overtraining, or injury. On the other hand, not pushing hard enough or acting too late might mean missing out on the performance improvements that come with proper adaptation. This balancing act highlights the importance of carefully monitoring and fine-tuning training for the best results. Striking the right balance requires real-time insights into an athlete’s health and performance, allowing coaches and practitioners to make informed decisions that optimise performance while managing the risk of injury.

Figure 1. The holistic performance puzzle that integrates load, load capacity, performance and health in sports. Red lines represent negative relationships. Green lines represent positive relationships. Adapted from Verhagen and Gabbett.⁸.

From metrics to insights: enhancing athlete health monitoring

When monitoring athletes, load and capacity to handle that load are crucial predictors. Performance and health are the outcomes we are aiming to optimise. High-performance teams regularly track key factors like training load, fatigue, sleep patterns and nutrition to understand how athletes respond to training and fine-tune their programmes for the best results.10,12

However, there is a lot of room for improvement in how we monitor athlete health. While tools like sleep quality assessments, heart rate variability (HRV) trackers and biomechanical movement analysis are widely used, they often fall short of supporting meaningful health-related decisions. A big limitation is how athlete health is typically classified—it is usually boiled down to two categories: ‘injured’ or ‘not injured.’ This black-and-white approach ignores the spectrum of health states and early warning signs, like fatigue or minor aches, which can signal a higher risk of injury.^{13 14}

Think of it this way: when it comes to performance, we recognise that performance is a continuum and that success on game day depends on a wide range of factors beyond physical readiness. Yet, in health monitoring, we still rely on simplistic binary classifications that do not fully capture an athlete’s health condition. This binary lens has been criticised for overlooking the complexities of injury risk and recovery.^{15 16} We argue that we must incorporate health metrics into a broader, more nuanced framework beyond binary injury outcomes. By doing so, we can address health concerns early and proactively, improve injury prevention efforts and bring health monitoring up to the same standard of precision we already use in performance tracking.

Refining our approach

We propose refining our approach to athlete screening and monitoring to overcome the shortcomings of traditional methods. Conventional models in sports medicine research often rely on linear relationships to analyse how load, capacity and injury risk interact.^{17 18} While useful, these models oversimplify the complex, interconnected factors at play.¹⁸ We suggest focusing more on precision and timing, moving away from broad, static assessments performed long before injuries arise.

As shown in figure 2, athlete health monitoring is a continuous process, combining historical and real-time data to predict future health outcomes. Collecting data daily, weekly or monthly—such as exposure to different training loads, sleep quality and motivation—aims to provide a more nuanced understanding of how athletes’ health responds to stressors and training stimuli.

Figure 2. Health monitoring is a continuous process that combines historical and real-time data to predict future health outcomes.

For this approach, the timing of data collection is critical. The most valuable insights come from data gathered ‘today,’ enabling real-time adjustments in high-performance settings. For instance, measuring HRV on the analysis day provides far more actionable insights than relying on outdated data, like sleep quality from 3 months ago. The further removed data are from the present moment, the less relevant it becomes. This argument, however, can also be turned around. A detailed measure like HRV is unlikely to describe an athlete’s overall injury risk. For this purpose, preseason screens like the drop jump (used to evaluate knee valgus and ACL injury risk) might be well suited. A detailed measure aligns with a detailed outcome, while a rough measure corresponds with a broader outcome.

What we need to change to make this approach work

Beyond binary: a spectrum-based approach to injury diagnosis

Traditional sports medicine tends to classify injuries dichotomously—athletes are either ‘injured’ or ‘not injured.’ While straightforward, this approach oversimplifies the complexity of human health and limits the effectiveness of monitoring and intervention strategies. A better approach recognises that injuries exist on a continuum, where early warning signs like mild discomfort or ‘niggles’ can develop into more severe conditions if left unaddressed.^{13 14 19}

Frameworks such as the Running Injury Continuum offer valuable insights by categorising injuries progressively, from optimal function to career-ending damage.¹³ This highlights the importance of early intervention—addressing minor issues early can prevent significant injuries and shorten recovery times. Similarly, the Multistate Framework for the Analysis of Subsequent Injury in Sports categorises athletes based on factors such as tissue damage, functional capacity and overall health.¹⁴ By moving away from binary classifications, these models provide a more complete understanding of injury risk and recovery dynamics.

Practical tools like the Oslo Sports Trauma Research Center Overuse Injury Questionnaire make monitoring issues that fall short of ‘injuries’ easier. For instance, tracking early signs of overuse or discomfort allows practitioners to act before problems aggravate.^{20 21} A study of Norway’s women’s premier league found that many athletes deal with health concerns that do not significantly affect participation but still deserve attention to avoid future complications.²² Incorporating such tools into monitoring systems gives athletes more control over their health while equipping practitioners with the data needed to provide targeted support.

To unlock the full potential of these approaches, we need to rethink injury monitoring systems. Effective systems should:

• Use valid, time-sensitive measures tailored to specific outcomes.

• Transition from binary to spectrum-based health classifications.

• Integrate early-warning signs to guide interventions.

By adopting this broader perspective, we can better support athletes in maintaining peak health and performance while reducing the risk of time-loss injuries.

Advancing statistical techniques

Artificial intelligence (AI) and machine learning (ML) promise to improve sports injury prevention and performance management. However, their impact has been limited by traditional screening models that rely on static, periodic assessments and dichotomous injury outcomes.23,25 These methods fail to reflect the dynamic, multifaceted nature of injury risk and performance variability. As a result, AI and ML models often produce vague or overly generalised predictions that do not help with day-to-day decision-making.²⁴ Without timely, context-specific data, these advanced technologies struggle to deliver actionable insights that can inform day-to-day decisions.

In our view, at present, other methods could be preferred to AI and ML. If we emphasise real-time data collection and contextually rich inputs, advanced statistical methods can effectively handle the complexity and ever-changing nature of athlete health and performance data. The following statistical approaches might be a good starting point.

• Latent Markov modelling helps identify hidden states or phases in an athlete’s condition over time.²⁶ For example, it can detect periods of heightened injury risk or optimal performance potential, even when the signals are subtle. By tracking transitions between health states, we can get insights into how short-term changes—like shifts in training load, recovery or external stressors—affect an athlete’s trajectory.

• Time-series analysis reveals patterns in physiological and performance data over time, while multilevel modelling accounts for nested structures, such as athletes within teams or training sessions within seasonal plans.²⁷

• Bayesian statistics can enhance the model by continuously updating risk predictions as new data comes in, creating a system that learns and adapts in real-time.

Final reflections

In this viewpoint, we proposed a practical, actionable framework for improving athlete health and performance management in sports settings. Focusing on real-time data, individualised monitoring and timely interventions shifts the emphasis from reactive measures to proactive strategies that can prevent injuries and optimise performance. While adopting such an approach may require adjustments to existing workflows and investments in validated tools, the benefits are clear: more precise, evidence-based decision-making directly supporting athletes’ day-to-day needs.