Citation

Wagemans J, De Leeuw AW, Catteeuw P, Vissers D. BMJ Open Sport Exerc. Med. 2023; 9(2): e001614.

Copyright

(Copyright © 2023, British Association of Sport and Exercise Medicine, Publisher BMJ Publishing Group)

DOI

10.1136/bmjsem-2023-001614

PMID

37397264

PMCID

PMC10314682

Abstract

OBJECTIVES: 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 [soccer] players.

METHODS: 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.

RESULTS: 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.

CONCLUSIONS: 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.

Language: en

Keywords

Injury; Machine learning; Football; Artificial Intelligence; Injury Prevention; Neuromuscular tests