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Fitness

Study Uses GPS Technology to Predict Injuries

New research indicates injuries could be predicted ahead of time in future.

Sam MacIntosh

Written by Sam MacIntosh Last updated on August 9, 2016

New research released in the British Journal of Sports Medicine indicates footballing injuries can be predicted by looking more closely at players’ workloads during training and competition.

The study’s findings are promising and indicate a new era in strength and conditioning research. [Photo credit: Pixabay]

In a joint effort between the University of Birmingham and Southampton Football Club, the study analysed the performance of youth players to observe the links between training and injury.

The researchers used a range of player performance data gathered by GPS equipment worn by the players in training, including: total distance covered; distance covered at high speed; total load/forces experienced; and short bursts of speed. This data was then analysed in relation to ‘recordable injuries’ – classified as mild, moderate, or severe – that caused the players to have to take time off from training. The injuries’ timeframe ranged from a couple of days to several weeks.

They found that high levels of acceleration over a three-week training period was the strongest indicator of overall and non-contact injury risk. It also found that a high amount of distance covered (in excess of 112km over a four-week period) and high weekly total loads significantly increased the risk of overall and non-contact injuries. Moderate-to-high levels of distance covered at high speed resulted in higher overall and non-contact injury incidence respectively; and very high weekly total loads and intense levels of short bursts of speed were significantly related to a higher risk of contact injury.

This study’s findings are promising and indicate a new era in strength and conditioning research. All athletes who can safely train harder can develop a greater tolerance for increasing intensity and the inevitable fatigue of competition. With this in mid, GPS technology could pave the way to a new and more resilient generation of athletes.

Lead researcher Laura Bowen commented:

“Our research has huge practical and scientific application. It expands on a recent body of literature in rugby league and cricket which has proposed that the prescription of workloads may be more indicative of injury than the load itself.”1

The specificity of the findings from the GPS technology can also be used to provide a set of thorough guidelines to help reduce the occurrence of injuries in elite youth football and beyond. The researchers have already been able to recommend that training should be organised so distance covered at high speed and total load experienced fluctuate across a four-week period, with more to come as the technology’s net is cast wider and more GPS studies become available.

References:

Laura Bowen, Aleksander Stefan Gross, Mo Gimpel, François-Xavier Li. Accumulated workloads and the acute:chronic workload ratio relate to injury risk in elite youth football players. British Journal of Sports Medicine, 2016; bjsports-2015-095820 DOI: 10.1136/bjsports-2015-095820

Sam MacIntosh

About Sam MacIntosh

Sam MacIntosh is a writer, editor, and a British Weightlifting and Precision Nutrition certified coach.

Sam played football from her pre-teens through to university, briefly switching to rugby when she was 25. It was then that she was introduced to strength training and weightlifting by a teammate and began training in CrossFit in late 2013. Shortly after, Chet Morjaria of Strength Education took her on as a powerlifting athlete and coached her to first in her weight class at the 2014 Welsh Powerlifting Open. She now trains in Olympic weightlifting and is the head barbell coach at CrossFit Gain in Norwich, England.

Sam is passionate about empowering athletes to think independently and neutralising nutritional propaganda. She's a recovering perfectionist, and enjoys coffee, books, and cinnamon donuts. You can connect with her on Twitter and Instagram.

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