IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v289y2020i2d10.1007_s10479-020-03560-5.html
   My bibliography  Save this article

Analysis of top kayakers’ training-intensity distribution and physiological adaptation based on structural modelling

Author

Listed:
  • Ruta Dadeliene

    (Vilnius University)

  • Stanislav Dadelo

    (Vilnius Gediminas Technical University)

  • Natalija Pozniak

    (Vilniaus Kolegija/University of Applied Sciences)

  • Leonidas Sakalauskas

    (Klaipeda University)

Abstract

High performance sport is important Analyse the effects of high intensity training on physical and functional capacities of elite kayakers by using the principal component analysis. The research analysed physical load during 1 year’s training cycle and used principal components analysis methods and Mann–Whitney Exact Test. The Principal component analysis approach revealed highly different adaptation of both well-trained athletes to the applied physical load. The coaches should pay more attention to individual skills of athletes, as well as to individual intensity and volume of the workout during the training sessions and the recovery time and quality. The Principal component algorithm suggested for monitoring and analysing the athletes’ training programs, as well as the findings of this study may be useful for planning the training programs.

Suggested Citation

  • Ruta Dadeliene & Stanislav Dadelo & Natalija Pozniak & Leonidas Sakalauskas, 2020. "Analysis of top kayakers’ training-intensity distribution and physiological adaptation based on structural modelling," Annals of Operations Research, Springer, vol. 289(2), pages 195-210, June.
  • Handle: RePEc:spr:annopr:v:289:y:2020:i:2:d:10.1007_s10479-020-03560-5
    DOI: 10.1007/s10479-020-03560-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-020-03560-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-020-03560-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Emilios Galariotis & Christophe Germain & Constantin Zopounidis, 2018. "A combined methodology for the concurrent evaluation of the business, financial and sports performance of football clubs: the case of France," Annals of Operations Research, Springer, vol. 266(1), pages 589-612, July.
    2. B. Mathew Brown & Mike Lauder & Rosemary Dyson, 2011. "Notational analysis of sprint kayaking: Differentiating between ability levels," International Journal of Performance Analysis in Sport, Taylor & Francis Journals, vol. 11(1), pages 171-183, April.
    3. Steven L. Fischer & Robin H. Hampton & Wayne J. Albert, 2014. "A simple approach to guide factor retention decisions when applying principal component analysis to biomechanical data," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 17(3), pages 199-203, February.
    4. Aleksandras Krylovas & Stanislavas Dadelo & Natalja Kosareva & Edmundas Kazimieras Zavadskas, 2017. "Entropy–KEMIRA Approach for MCDM Problem Solution in Human Resources Selection Task," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 16(05), pages 1183-1209, September.
    5. Túlio A. M. Toffolo & Jan Christiaens & Frits C. R. Spieksma & Greet Vanden Berghe, 2019. "The sport teams grouping problem," Annals of Operations Research, Springer, vol. 275(1), pages 223-243, April.
    6. Özmen, Ayşe & Yılmaz, Yavuz & Weber, Gerhard-Wilhelm, 2018. "Natural gas consumption forecast with MARS and CMARS models for residential users," Energy Economics, Elsevier, vol. 70(C), pages 357-381.
    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. Emilia Vann Yaroson & Soumyadeb Chowdhury & Sachin Kumar Mangla & Prasanta Kumar Dey, 2024. "Unearthing the interplay between organisational resources, knowledge and industry 4.0 analytical decision support tools to achieve sustainability and supply chain wellbeing," Annals of Operations Research, Springer, vol. 342(2), pages 1321-1368, November.
    2. Soumyadeb Chowdhury & Oscar Rodriguez-Espindola & Prasanta Dey & Pawan Budhwar, 2023. "Blockchain technology adoption for managing risks in operations and supply chain management: evidence from the UK," Annals of Operations Research, Springer, vol. 327(1), pages 539-574, August.
    3. Aleksandras Krylovas & Natalja Kosareva & Stanislav Dadelo, 2020. "European Countries Ranking and Clustering Solution by Children’s Physical Activity and Human Development Index Using Entropy-Based Methods," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
    4. Abadie, Amelie & Roux, Mélanie & Chowdhury, Soumyadeb & Dey, Prasanta, 2023. "Interlinking organisational resources, AI adoption and omnichannel integration quality in Ghana’s healthcare supply chain," Journal of Business Research, Elsevier, vol. 162(C).
    5. Jovilė Barevičiūtė & Stanislav Dadelo & Vaida Asakavičiūtė, 2023. "The Skills of Critical Thinking, Creativity, and Communication as Tools for Overcoming Social Simulation in the Context of Sustainability: A Case Study of Students’ Self-Assessment of the Affective Do," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    6. Pierpaolo D’Urso & Livia Giovanni & Vincenzina Vitale, 2023. "A robust method for clustering football players with mixed attributes," Annals of Operations Research, Springer, vol. 325(1), pages 9-36, June.
    7. Chowdhury, Soumyadeb & Budhwar, Pawan & Dey, Prasanta Kumar & Joel-Edgar, Sian & Abadie, Amelie, 2022. "AI-employee collaboration and business performance: Integrating knowledge-based view, socio-technical systems and organisational socialisation framework," Journal of Business Research, Elsevier, vol. 144(C), pages 31-49.
    8. Chowdhury, Soumyadeb & Dey, Prasanta Kumar & Rodríguez-Espíndola, Oscar & Parkes, Geoff & Tuyet, Nguyen Thi Anh & Long, Dang Duc & Ha, Tran Phuong, 2022. "Impact of Organisational Factors on the Circular Economy Practices and Sustainable Performance of Small and Medium-sized Enterprises in Vietnam," Journal of Business Research, Elsevier, vol. 147(C), pages 362-378.

    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. Guillermo Durán, 2021. "Sports scheduling and other topics in sports analytics: a survey with special reference to Latin America," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 125-155, April.
    2. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
    3. Wei, Nan & Li, Changjun & Peng, Xiaolong & Li, Yang & Zeng, Fanhua, 2019. "Daily natural gas consumption forecasting via the application of a novel hybrid model," Applied Energy, Elsevier, vol. 250(C), pages 358-368.
    4. Li, Wei & Lu, Can, 2019. "The multiple effectiveness of state natural gas consumption constraint policies for achieving sustainable development targets in China," Applied Energy, Elsevier, vol. 235(C), pages 685-698.
    5. Oleksandr Castello & Marina Resta, 2023. "A Machine-Learning-Based Approach for Natural Gas Futures Curve Modeling," Energies, MDPI, vol. 16(12), pages 1-22, June.
    6. Soltanisarvestani, A. & Safavi, A.A., 2021. "Modeling unaccounted-for gas among residential natural gas consumers using a comprehensive fuzzy cognitive map," Utilities Policy, Elsevier, vol. 72(C).
    7. Dries Goossens & Jeroen Beliën, 2023. "Teaching Integer Programming by Scheduling the Belgian Soccer League," INFORMS Transactions on Education, INFORMS, vol. 23(3), pages 164-172, May.
    8. Valerio Ficcadenti & Roy Cerqueti & Ciro Hosseini Varde’i, 2023. "A rank-size approach to analyse soccer competitions and teams: the case of the Italian football league “Serie A"," Annals of Operations Research, Springer, vol. 325(1), pages 85-113, June.
    9. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
    10. Fang, Xichen & Guo, Hongye & Zheng, Kedi & Liu, Shuangquan & Chen, Qixin, 2024. "Real-time capacity cost obligations design in high-renewables energy markets," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    11. Lu, Hongfang & Ma, Xin & Azimi, Mohammadamin, 2020. "US natural gas consumption prediction using an improved kernel-based nonlinear extension of the Arps decline model," Energy, Elsevier, vol. 194(C).
    12. Palma, Alessia & Paltrinieri, Andrea & Goodell, John W. & Oriani, Marco Ercole, 2024. "The black box of natural gas market: Past, present, and future," International Review of Financial Analysis, Elsevier, vol. 94(C).
    13. Xi, Xian & Zhou, Jinsheng & Gao, Xiangyun & Liu, Donghui & Zheng, Huiling & Sun, Qingru, 2019. "Impact of changes in crude oil trade network patterns on national economy," Energy Economics, Elsevier, vol. 84(C).
    14. Ayşe Özmen, 2023. "Sparse regression modeling for short- and long‐term natural gas demand prediction," Annals of Operations Research, Springer, vol. 322(2), pages 921-946, March.
    15. Wei, Nan & Yin, Lihua & Li, Chao & Liu, Jinyuan & Li, Changjun & Huang, Yuanyuan & Zeng, Fanhua, 2022. "Data complexity of daily natural gas consumption: Measurement and impact on forecasting performance," Energy, Elsevier, vol. 238(PC).
    16. Ahmat Khazali Acyl & Flavian Emmanuel Sapnken & Aubin Kinfack Jeutsa & Jean Marie Stevy Sama & Marcel Rodrigue Ewodo-Amougou & Jean Gaston Tamba, 2024. "Forecasting Petroleum Products Consumption in the Chadian Road Transport Sector using Optimised Grey Models," International Journal of Energy Economics and Policy, Econjournals, vol. 14(1), pages 603-611, January.
    17. Luo, Keyu & Guo, Qiang & Li, Xiafei, 2022. "Can the return connectedness indices from grey energy to natural gas help to forecast the natural gas returns?," Energy Economics, Elsevier, vol. 109(C).
    18. Ayşe Özmen & Yuriy Zinchenko & Gerhard-Wilhelm Weber, 2023. "Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market," Annals of Operations Research, Springer, vol. 324(1), pages 1337-1367, May.
    19. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    20. Ding, Lili & Zhao, Zhongchao & Wang, Lei, 2022. "Probability density forecasts for natural gas demand in China: Do mixed-frequency dynamic factors matter?," Applied Energy, Elsevier, vol. 312(C).

    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:spr:annopr:v:289:y:2020:i:2:d:10.1007_s10479-020-03560-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.