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Smartphone data could help predict injury risk

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Smartphones could be used to help predict when an athlete is likely to be injured, according to a study published in the journal Risk Analysis.

Researchers at the University of Tennessee Chattanooga say that injury risk screening procedures can be administered using wireless devices, such as smartphones, connected to a cloud server.

This approach takes into account the athlete’s previous injury history and data gathered from their smartphone, together with injury surveillance data. Various screening tests may be combined from different sources to create a real-time “dashboard” of an athlete’s status.

This data may then help identify athletes with higher injury risk, supporting efforts to reduce the financial, emotional and physical toll of sports injuries.

As the Society for Risk Analysis explains, conventional sports injury management relies heavily on subjective assessment, such as the athlete’s verbal description of pain and discomfort. However, athletes may not always assess their own abilities and injuries accurately. They may start competing before they are ready, which can increase their risk of re-injury. Currently, the most widely used method for assessing injury risk is the Functional Movement Screen, which generates a subjective score based on observed ability to complete the movements.

In a study involving 43 American football players, researchers Gary B. Wilkerson and Marisa A. Colston used a smartphone accelerometer to gather performance data on each individual in a single-leg postural stability test. To understand the factors influencing an athlete’s risk of injury, this data was integrated with self-reports of previous injuries and longitudinal tracking of exposure to game conditions.

Analysing the data, the researchers found that athletes who played at least eight games in a 13-game season had more than three times greater odds of injury occurrence than those who played fewer than eight games. Among athletes who exhibited at least one risk factor, 42% sustained an injury.

“Assigning all athletes to a single type of training programme, without consideration of an individual’s unique risk profile, may fail to produce a substantial decrease in injury likelihood,” said Wilkerson, lead author and professor in a graduate athletic training programme. “The results also provide a useful estimation of the odds of injury occurrence for each athlete during the subsequent season.”

https://www.prnewswire.com/news-releases/what-if-we-could-predict-when-an-athlete-was-going-to-be-injured-300609379.html

https://onlinelibrary.wiley.com/doi/abs/10.1111/risa.12984