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Record W2890321975

Influence of fatigue on sprint acceleration mechanics: is there a connection with hamstring injury?

2018· article· en· W2890321975 on OpenAlexaboutno aff
Julien Paulus, Cédric Schwartz, Jean‐François Kaux, François Tubez, Jean‐Louis Croisier

Bibliographic record

VenueORBi (University of Liège) · 2018
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsnot available
Fundersnot available
KeywordsSprintAccelerationConnection (principal bundle)HamstringPhysical medicine and rehabilitationMedicineEngineeringStructural engineeringPhysical therapyPhysicsClassical mechanics
DOInot available

Abstract

fetched live from OpenAlex

Introduction The relationship between hamstring injury and their capacity to produce a force are no longer to demonstrate: decreased ability to produce strength after muscle injury [1], increased risk of injury in case of weakness and/or isokinetic imbalance [2-4], eccentric strength weakness increasing the risk of muscle damage [5-7],… Several studies have also highlighted the fact that fatigue induced by sports activities would increase the risk of hamstring injuries [8, 9]. During a sprint, the ability to orient the forces horizontally, telltale of the effectiveness of the foot strikes [10], is related to the force production capacity of the hamstrings [11]. Moreover, two studies, a case report [12] and a preliminary study [13], seem to indicate that an alteration in horizontal force production during sprint occurs before and after hamstring injury. Is a progressive induction of fatigue lead to a decrease in the athlete's ability to produce horizontally oriented forces during a sprint and in this case could increase the risk of injury? Methods Seven amateur soccer player (22.7 ± 1.3 years, 179.3 ± 5.5 cm, 75.4 ± 4.6 kg) realized the Soccer-specific Aerobic Field Test (SAFT90) [9, 14] with three maximal 50m sprints before, one every each 15 minutes during and three after the protocol. The force- and power-velocity relationships and mechanical effectiveness of force application during sprint running are calculated from anthropometric and spatio-temporal data acquired with a Stalker ATS II radar [15]. Results The Repeated Measures ANOVA reveals a significant (p < 0.001) time dependent decrease in theoretical maximal velocity (v0) (-11.0%), in maximal velocity reached at the end of the acceleration (vHmax) (-10.2%) and in ratio of the net horizontal force (RF0) (-10.5%). Conversely, there's no time dependent modification in theoretical maximal force (F0) (-9.8%), in acceleration time constant (τ) (-18.2%) and in resultant ground reaction forces (GRF) (-3.3%). Discussion Our results, time dependent decrease in RF0, revealed that the fatigue, induced by SAFT90, impacts particularly the hip extensors since at the same time the GRF, resultant ground reaction forces, isn't significantly reduced by the induction of fatigue. Based on previous studies [12, 13], these findings about decreased strength production capacity of hamstring refine our knowledge of the relationships between exhaustion, decreased performance and increased predisposition to hamstring strain injury as the soccer game progresses. Indeed, this is the first time, at our knowledge, that the strength production capabilities of hip extensors are measured accurately during the sprint, the pattern responsible for the greatest number of hamstring injuries in football [16]. Acknowledgements The authors wish to thank the Wallonia-Brussels Federation for their assistance in this study. References 1. Maniar, N., et al., Hamstring strength and flexibility after hamstring strain injury: A systematic review and meta-analysis. British Journal of Sports Medicine, 2016. 2. Croisier, J.L., et al., Strength imbalances and prevention of hamstring injury in professional soccer players: A prospective study. Am J Sports Med, 2008. 36(8): p. 1469-75. 3. van Dyk, N., et al., Hamstring and quadriceps isokinetic strength deficits are weak risk factors for hamstring strain injuries: A 4-year cohort study. Am J Sports Med, 2016. 44(7): p. 1789-95. 4. Yeung, S.S., A.M. Suen, and E.W. Yeung, A prospective cohort study of hamstring injuries in competitive sprinters: Preseason muscle imbalance as a possible risk factor. Br J Sports Med, 2009. 43(8): p. 589-94. 5. Bourne, M.N., et al., Eccentric knee flexor strength and risk of hamstring injuries in rugby union: A prospective study. Am J Sports Med, 2015. 43(11): p. 2663-70. 6. Opar, D.A., et al., Eccentric hamstring strength and hamstring injury risk in Australian footballers. Med Sci Sports Exerc, 2015. 47(4): p. 857-65. 7. Timmins, R.G., et al., Short biceps femoris fascicles and eccentric knee flexor weakness increase the risk of hamstring injury in elite football (soccer): A prospective cohort study. British Journal of Sports Medicine, 2015. 8. Greig, M. and J.C. Siegler, Soccer-specific fatigue and eccentric hamstrings muscle strength. Journal of Athletic Training, 2009. 44(2): p. 180-184. 9. Small, K., et al., Soccer fatigue, sprinting and hamstring injury risk. Int J Sports Med, 2009. 30(8): p. 573-8. 10. Morin, J.B., et al., Mechanical determinants of 100-m sprint running performance. Eur J Appl Physiol, 2012. 112(11): p. 3921-30. 11. Morin, J.-B., et al., Sprint acceleration mechanics: The major role of hamstrings in horizontal force production. Frontiers in Physiology, 2015. 6: p. 404. 12. Mendiguchia, J., et al., Field monitoring of sprinting power-force-velocity profile before, during and after hamstring injury: two case reports. J Sports Sci, 2016. 34(6): p. 535-41. 13. Edouard, P. and J.-B. Morin, Preventing hamstring muscle injuries by sprint acceleration performance evaluation: What? How? When?, in IOC World Conference on Prevention of Injury & Illness in sport. 2017: Monaco. 14. Lovell, R., B. Knapper, and K. Small, Physiological responses to SAFT90: A new soccer-specific match simulation. Coaching and Sports Science, 2008. 3: p. 46-67. 15. Samozino, P., et al., A simple method for measuring power, force, velocity properties, and mechanical effectiveness in sprint running. Scand J Med Sci Sports, 2016. 26(6): p. 648-58. 16. Ekstrand, J., M. Hagglund, and M. Walden, Epidemiology of muscle injuries in professional football (soccer). Am J Sports Med, 2011. 39(6): p. 1226-32.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.249
Teacher spread0.231 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Published2018
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