IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v16y2019i23p4853-d293381.html
   My bibliography  Save this article

Effect of Training Load on Post-Exercise Cardiac Troponin T Elevations in Young Soccer Players

Author

Listed:
  • Rafel Cirer-Sastre

    (National Institute of Physical Education of Catalonia (INEFC), University of Lleida (UdL), Partida la Caparrella s/n, E-25192 Lleida, Spain
    Research Group Human Movement (RGHM), Universitat de Lleida (UdL), Plaça de Víctor Siurana, 25003 Lleida, Spain)

  • Alejandro Legaz-Arrese

    (Section of Physical Education and Sports, University of Zaragoza, Calle de Pedro Cerbuna, 50009 Zaragoza, Spain)

  • Francisco Corbi

    (National Institute of Physical Education of Catalonia (INEFC), University of Lleida (UdL), Partida la Caparrella s/n, E-25192 Lleida, Spain
    Research Group Human Movement (RGHM), Universitat de Lleida (UdL), Plaça de Víctor Siurana, 25003 Lleida, Spain)

  • Isaac López-Laval

    (Section of Physical Education and Sports, University of Zaragoza, Calle de Pedro Cerbuna, 50009 Zaragoza, Spain)

  • Jose Puente-Lanzarote

    (Lozano Blesa University Hospital, Avda. San Juan Bosco, 50009 Zaragoza, Spain)

  • Vicenç Hernández-González

    (Research Group Human Movement (RGHM), Universitat de Lleida (UdL), Plaça de Víctor Siurana, 25003 Lleida, Spain
    Section of Physical Education, Universitat de Lleida (UdL), Plaça de Víctor Siurana, 25003 Lleida, Spain)

  • Joaquín Reverter-Masià

    (Research Group Human Movement (RGHM), Universitat de Lleida (UdL), Plaça de Víctor Siurana, 25003 Lleida, Spain
    Section of Physical Education, Universitat de Lleida (UdL), Plaça de Víctor Siurana, 25003 Lleida, Spain)

Abstract

Training load (TL) metrics are usually assessed to estimate the individual, physiological and psychological, acute, and adaptive responses to training. Cardiac troponins (cTn) reflect myocardial damage and are routinely analyzed for the clinical diagnosis of myocardial injury. The association between TL and post-exercise cTn elevations is scarcely investigated in young athletes, especially after playing common team sports such as soccer. The objective of this study was to assess the relationship between TL measurements during a small-sided soccer game and the subsequent increase in cTn in young players. Twenty male soccer players (age 11.9 ± 2 years, height 151 ± 13 cm, weight 43 ± 13 kg) were monitored during a 5 × 5 small-sided game and had blood samples drawn before, immediately after, and 3 h after exercise for a posterior analysis of high-sensitivity cardiac troponin T (hs-cTnT). Internal, external, and mixed metrics of TL were obtained from the rating of perceived exertion (RPE), heart rate (HR), and GPS player tracking. The results show that the concentration of hs-cTnT peaked at 3 h post-exercise in all participants. The magnitude of hs-cTnT elevation was mainly explained by the exercise duration in the maximal heart rate zone (Maximum Probability of Effect (MPE) = 92.5%), time in the high-speed zone (MPE = 90.4 %), and distance in the high-speed zone (MPE = 90.45%). Our results support the idea that common metrics of TL in soccer, easily obtained using player tracking systems, are strongly associated with the release of hs-cTnT in children and adolescents.

Suggested Citation

  • Rafel Cirer-Sastre & Alejandro Legaz-Arrese & Francisco Corbi & Isaac López-Laval & Jose Puente-Lanzarote & Vicenç Hernández-González & Joaquín Reverter-Masià, 2019. "Effect of Training Load on Post-Exercise Cardiac Troponin T Elevations in Young Soccer Players," IJERPH, MDPI, vol. 16(23), pages 1-10, December.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4853-:d:293381
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/16/23/4853/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/16/23/4853/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Rafel Cirer-Sastre & Alejandro Legaz-Arrese & Francisco Corbi & Isaac López-Laval & Juan José Puente-Lanzarote & Vicenç Hernández-González & Joaquin Reverter-Masia, 2020. "Cardiac Troponin T Release after Football 7 in Healthy Children and Adults," IJERPH, MDPI, vol. 17(3), pages 1-9, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Francis,David C. & Kubinec ,Robert, 2022. "Beyond Political Connections : A Measurement Model Approach to Estimating Firm-levelPolitical Influence in 41 Economies," Policy Research Working Paper Series 10119, The World Bank.
    2. Martinovici, A., 2019. "Revealing attention - how eye movements predict brand choice and moment of choice," Other publications TiSEM 7dca38a5-9f78-4aee-bd81-c, Tilburg University, School of Economics and Management.
    3. Yongping Bao & Ludwig Danwitz & Fabian Dvorak & Sebastian Fehrler & Lars Hornuf & Hsuan Yu Lin & Bettina von Helversen, 2022. "Similarity and Consistency in Algorithm-Guided Exploration," CESifo Working Paper Series 10188, CESifo.
    4. Torsten Heinrich & Jangho Yang & Shuanping Dai, 2020. "Growth, development, and structural change at the firm-level: The example of the PR China," Papers 2012.14503, arXiv.org.
    5. van Kesteren Erik-Jan & Bergkamp Tom, 2023. "Bayesian analysis of Formula One race results: disentangling driver skill and constructor advantage," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 19(4), pages 273-293, December.
    6. Xin Xu & Yang Lu & Yupeng Zhou & Zhiguo Fu & Yanjie Fu & Minghao Yin, 2021. "An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    7. Xiaoyue Xi & Simon E. F. Spencer & Matthew Hall & M. Kate Grabowski & Joseph Kagaayi & Oliver Ratmann & Rakai Health Sciences Program and PANGEA‐HIV, 2022. "Inferring the sources of HIV infection in Africa from deep‐sequence data with semi‐parametric Bayesian Poisson flow models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 517-540, June.
    8. Kuschnig, Nikolas, 2021. "Bayesian Spatial Econometrics and the Need for Software," Department of Economics Working Paper Series 318, WU Vienna University of Economics and Business.
    9. Deniz Aksoy & David Carlson, 2022. "Electoral support and militants’ targeting strategies," Journal of Peace Research, Peace Research Institute Oslo, vol. 59(2), pages 229-241, March.
    10. Richard Hunt & Shelton Peiris & Neville Weber, 2022. "Estimation methods for stationary Gegenbauer processes," Statistical Papers, Springer, vol. 63(6), pages 1707-1741, December.
    11. D. Fouskakis & G. Petrakos & I. Rotous, 2020. "A Bayesian longitudinal model for quantifying students’ preferences regarding teaching quality indicators," METRON, Springer;Sapienza Università di Roma, vol. 78(2), pages 255-270, August.
    12. Joseph B. Bak-Coleman & Ian Kennedy & Morgan Wack & Andrew Beers & Joseph S. Schafer & Emma S. Spiro & Kate Starbird & Jevin D. West, 2022. "Combining interventions to reduce the spread of viral misinformation," Nature Human Behaviour, Nature, vol. 6(10), pages 1372-1380, October.
    13. Jonas Moss & Riccardo De Bin, 2023. "Modelling publication bias and p‐hacking," Biometrics, The International Biometric Society, vol. 79(1), pages 319-331, March.
    14. Gael M. Martin & David T. Frazier & Christian P. Robert, 2020. "Computing Bayes: Bayesian Computation from 1763 to the 21st Century," Monash Econometrics and Business Statistics Working Papers 14/20, Monash University, Department of Econometrics and Business Statistics.
    15. David M. Phillippo & Sofia Dias & A. E. Ades & Mark Belger & Alan Brnabic & Alexander Schacht & Daniel Saure & Zbigniew Kadziola & Nicky J. Welton, 2020. "Multilevel network meta‐regression for population‐adjusted treatment comparisons," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 183(3), pages 1189-1210, June.
    16. Matthias Breuer & Harm H. Schütt, 2023. "Accounting for uncertainty: an application of Bayesian methods to accruals models," Review of Accounting Studies, Springer, vol. 28(2), pages 726-768, June.
    17. Loke Schmalensee & Pauline Caillault & Katrín Hulda Gunnarsdóttir & Karl Gotthard & Philipp Lehmann, 2023. "Seasonal specialization drives divergent population dynamics in two closely related butterflies," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    18. Edgar Santos‐Fernandez & Erin E. Peterson & Julie Vercelloni & Em Rushworth & Kerrie Mengersen, 2021. "Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(1), pages 147-173, January.
    19. Barakat, Bilal Fouad & Dharamshi, Ameer & Alkema, Leontine & Antoninis, Manos, 2021. "Adjusted Bayesian Completion Rates (ABC) Estimation," SocArXiv at368, Center for Open Science.
    20. Burbano, Vanessa & Padilla, Nicolas & Meier, Stephan, 2020. "Gender Differences in Preferences for Meaning at Work," IZA Discussion Papers 13053, Institute of Labor Economics (IZA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4853-:d:293381. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.