The science of injury versus load management


In 1989, Stephen Covey published his highly acclaimed book,  The 7 Habits of Highly Effective People. This book, which has sold over 25 million copies, explores how individuals contribute to successful organisations by fostering interdependence among staff.

With the rapid growth of sports medicine teams, sport has become an ‘arms race’, with team owners often believing that the secret to developing a winning franchise lies in the quality of the backroom staff.

This topic will persist until all related doubts are progressively resolved, taking diverse variables into consideration at various time frames. A practical approach is key.

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It’s a battle of data versus application. Over the last two decades, research on the relationship between training load and injuries has grown exponentially. In India, this trend has become prominent in the last four to six years, showing where the country stands in the practical application and data collection in sports science.

As we gather more data, our understanding of training modules has improved. However, there is still a long way to go in collecting data relevant to Indian athletes. No data is wasted if it can be used to achieve peak performance with the goal of winning.

The entire thought process has gone through a paradigm shift in the last two decades. Earlier, injuries were directly related to training intensity, load, incorrect methods, and exercise choices. Now, injuries can be attributed to sudden spikes in training load when an athlete is accustomed to a certain load.

In this context, it appears that overuse injuries may arise from athletes being underprepared for the load they are about to perform. A question of interest to both strength and conditioning (S&C) coaches/physios and researchers is why some athletes sustain injuries at low training loads, while others can tolerate much greater loads. What differences change the perspective in training protocols?

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Incorporating a higher chronic training load, well-developed aerobic fitness, and good lower body strength appear to moderate the training-injury relationship and provide a shielding effect against spikes in load.

The training-performance conundrum is complex and dynamic, with multiple inputs to injury and performance at any given time.

The challenge faced by sports science researchers is obtaining large enough longitudinal data sets to capture the time-varying nature of physiological and musculoskeletal capacities and training-load data to sufficiently inform injury-prevention efforts.

The training to performance enigma can be solved through collaboration between researchers, support staff, and clinicians, as well as understanding that efficacy does not equate to effectiveness.

To find an ideal pattern in training to performance domain, large amounts of data need to be collated over time, considering many variables from sport to sport, skill to skill, and individual to individual. The effectiveness of training modes can be derived through the correct inference of the data collected.

From an Indian perspective related to data collection and load management principles, we need to develop our own system with a foolproof process tailored to our athletes. Borrowing old data from other countries proves futile in the long run and detrimental to our sports science ecosystem.

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This hard and laborious process is necessary if we want to compete with the best. With the dedication and passion of the younger generation, future performance for India can improve, or else we will continue to face enquiry commissions for our poor performance as we always have.

Change can come from the younger generation passionate about sports science. Many self-proclaimed experts on social media seek financial security and brand building by currying favour with selected players.

These professionals can be dangerous for trusting sports persons. Proper due diligence is needed before involving them in mainstream sports science. Ultimately, what matters is creating history for India, rather than economics for individuals.

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