Power Profile Index: An Adjustable Metric for Load Monitoring in Road Cycling

  1. Mateo-March, Manuel
  2. Lillo-Beviá, José Ramón
  3. della Mattia, Gabriel
  4. Muriel, Xabier
  5. Barranco-Gil, David
  6. Zabala, Mikel
  7. Courel-Ibáñez, Javier
  8. Salas-Montoro, José Antonio
  1. 1 Universidad Miguel Hernández de Elche
    info

    Universidad Miguel Hernández de Elche

    Elche, España

    ROR https://ror.org/01azzms13

  2. 2 Universidad Europea de Madrid
    info

    Universidad Europea de Madrid

    Madrid, España

    ROR https://ror.org/04dp46240

  3. 3 Universitat d'Alacant
    info

    Universitat d'Alacant

    Alicante, España

    ROR https://ror.org/05t8bcz72

  4. 4 EDNacore AI, AGMT2 Human Performance, Pompano, FL 33062, USA
  5. 5 Universidad de Murcia
    info

    Universidad de Murcia

    Murcia, España

    ROR https://ror.org/03p3aeb86

  6. 6 Universidad de Granada
    info

    Universidad de Granada

    Granada, España

    ROR https://ror.org/04njjy449

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Revista:
Applied Sciences

ISSN: 2076-3417

Año de publicación: 2022

Volumen: 12

Número: 21

Páginas: 11020

Tipo: Artículo

DOI: 10.3390/APP122111020 GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Applied Sciences

Resumen

Workload is calculated from exercise volume and intensity. In endurance sports, intensity has been measured using heart rate or RPE, giving rise to load indexes such as sRPE or TRIMP. In cycling, the advent of power meters led to new indexes, such as TSS. All these indexes have limitations, especially for high intensity exercise. Therefore, a new index for cycling is proposed, the Power Profile Index (PPi), which includes a weighting factor obtained from the relative exercise intensity and stage type. Using power data from 67 WorldTour cyclists and fatigue records in different stage types from 102 road cyclists, weighting factors for intensity and stage type were determined. Subsequently, the PPi was computed and compared to current indexes using data from a WorldTour team during the 2018 Tour de France. The proposed index showed a strong correlation with perceived fatigue as a function of stage type (R2 = 0.9996), as well as no differences in the load quantification in different types of stage profiles (p = 0.292), something that does not occur with other indexes such as TSS, RPE, or eTRIMP (p < 0.001). Therefore, PPi is a new index capable of quantifying the high intensity efforts that produce greater fatigue, as well as considering the stage type.

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