Literature DB >> 35120156

Use of GPS to measure external load and estimate the incidence of muscle injuries in men's football: A novel descriptive study.

Marc Guitart1, Martí Casals1,2,3, David Casamichana4, Jordi Cortés5, Francesc Xavier Valle1, Alan McCall6, Francesc Cos3, Gil Rodas1,7.   

Abstract

Measurement of external load in players provides objective information to optimise the weekly balance between training and recovery to improve performance and prevent injuries. Our aim was to evaluate the incidence of sports-related muscle injuries of the lower limb in relation to external load, measured by global positioning system (GPS), in football players. A descriptive study was carried out. Data were collected from 71 professional male football players (30 professionals and 41 youth players) from an elite football club competing in the Spanish and European League in the 2017-2018 season. As external load variables, we measured High Metabolic Load Distance (HMLD), High Speed Running (HSR), Player Load (PL), and Total Distance (TD) through GPS. Injury rate (IR) was calculated both in relation to such GPS load metrics and to load exposure time. We considered categories (youth and professional), playing positions (centre back, full back, midfielder, and forward), and training day with respect to match-day (-4MD, -3MD, -2MD, -1MD, MD, +1MD, +2MD). The GPS load metrics HMLD, HSR, PL, and TD showed very similar patterns across categories and positions, but varied according to training session or MD. The highest loads were observed on MD and three days prior to the match (-3MD). Similarly, the overall IR, both calculated per load exposure time and per GPS load metrics, was highest on MD and -3MD. Again, no differences were observed between youth and professional players. Midfielders demonstrated the highest IR in all metrics, followed by the forwards. In conclusion, this study suggests that external load and incidence of muscle injuries are directly proportional. Therefore, the measurement of more external load variables other than load exposure time, such as the GPS metrics HMLD, HSR, PL, and TD may help to describe the pattern and magnitude of injuries. Future studies based on ours may help to further improve the understanding of the incidence of muscle injuries on the basis of external loads measurements in different football teams.

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Year:  2022        PMID: 35120156      PMCID: PMC8815982          DOI: 10.1371/journal.pone.0263494

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  21 in total

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Journal:  J Sports Sci       Date:  2003-07       Impact factor: 3.337

2.  The Football Association medical research programme: an audit of injuries in academy youth football.

Authors:  R J Price; R D Hawkins; M A Hulse; A Hodson
Journal:  Br J Sports Med       Date:  2004-08       Impact factor: 13.800

3.  Hamstring injuries have increased by 4% annually in men's professional football, since 2001: a 13-year longitudinal analysis of the UEFA Elite Club injury study.

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Journal:  Br J Sports Med       Date:  2016-01-08       Impact factor: 13.800

4.  Performance characteristics according to playing position in elite soccer.

Authors:  V Di Salvo; R Baron; H Tschan; F J Calderon Montero; N Bachl; F Pigozzi
Journal:  Int J Sports Med       Date:  2006-10-06       Impact factor: 3.118

5.  Time-motion and physiological profile of football training sessions performed by under-15, under-17 and under-19 elite Portuguese players.

Authors:  Eduardo A Abade; Bruno V Gonçalves; Nuno M Leite; Jaime E Sampaio
Journal:  Int J Sports Physiol Perform       Date:  2013-08-06       Impact factor: 4.010

6.  Small-Sided Games in Elite Soccer: Does One Size Fit All?

Authors:  Mathieu Lacome; Ben M Simpson; Yannick Cholley; Philippe Lambert; Martin Buchheit
Journal:  Int J Sports Physiol Perform       Date:  2018-05-26       Impact factor: 4.010

7.  Match-play activity profile in professional soccer players during official games and the relationship between external and internal load.

Authors:  L Suarez-Arrones; N Torreño; B Requena; E Sáez De Villarreal; D Casamichana; J C Barbero-Alvarez; D Munguía-Izquierdo
Journal:  J Sports Med Phys Fitness       Date:  2014-10-07       Impact factor: 1.637

8.  Prediction of Overuse Injuries in Professional U18-U21 Footballers Using Metrics of Training Distance and Intensity.

Authors:  Christopher S Bacon; Alexis R Mauger
Journal:  J Strength Cond Res       Date:  2017-11       Impact factor: 3.775

9.  Injury incidence and injury patterns in professional football: the UEFA injury study.

Authors:  J Ekstrand; M Hägglund; M Waldén
Journal:  Br J Sports Med       Date:  2009-06-23       Impact factor: 13.800

10.  Internal and External Training Load in Under-19 versus Professional Soccer Players during the In-Season Period.

Authors:  Sullivan Coppalle; Guillaume Ravé; Jason Moran; Iyed Salhi; Abderraouf Ben Abderrahman; Sghaeir Zouita; Urs Granacher; Hassane Zouhal
Journal:  Int J Environ Res Public Health       Date:  2021-01-11       Impact factor: 3.390

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Journal:  Nutrients       Date:  2022-05-21       Impact factor: 6.706

2.  Machine Learning for Predicting Lower Extremity Muscle Strain in National Basketball Association Athletes.

Authors:  Yining Lu; Ayoosh Pareek; Ophelie Z Lavoie-Gagne; Enrico M Forlenza; Bhavik H Patel; Anna K Reinholz; Brian Forsythe; Christopher L Camp
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3.  A targeted metabolic analysis of football players and its association to player load: Comparison between women and men profiles.

Authors:  Gil Rodas; Eva Ferrer; Xavier Reche; Juan Daniel Sanjuan-Herráez; Alan McCall; Guillermo Quintás
Journal:  Front Physiol       Date:  2022-09-30       Impact factor: 4.755

  3 in total

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