Development of an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players

• Published in: BMJ Open Sport & Exercise Medicine 2023, Vol.9 (2), p.e001614-e001614
• Main Authors: Wagemans, Jente, De Leeuw, Arie-Willem, Catteeuw, Peter, Vissers, Dirk
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Cohort analysis; Data collection; Heart rate; Injury prevention; Machine learning; Methods; Original Research; Professional football; Risk factors; Sports injuries; Variables
• ISSN: 2055-7647; 2055-7647
• DOI: 10.1136/bmjsem-2023-001614

This retrospective cohort study explored an algorithm-based approach using neuromuscular test results to indicate an increased risk for non-contact lower limb injuries in elite football players. Neuromuscular data (eccentric hamstring strength, isometric adduction and abduction strength and countermovement jump) of 77 professional male football players were assessed at the start of the season (baseline) and, respectively, at 4, 3, 2 and 1 weeks before the injury. We included 278 cases (92 injuries; 186 healthy) and applied a subgroup discovery algorithm. More injuries occurred when between-limb abduction imbalance 3 weeks before injury neared or exceeded baseline values (threshold≥0.97), or adduction muscle strength of the right leg 1 week before injury remained the same or decreased compared with baseline values (threshold≤1.01). Moreover, in 50% of the cases, an injury occurred if abduction strength imbalance before the injury is over 97% of the baseline values and peak landing force in the left leg 4 weeks before the injury is lower than 124% compared with baseline. This exploratory analysis provides a proof of concept demonstrating that a subgroup discovery algorithm using neuromuscular tests has potential use for injury prevention in football.