Internal Training Load Affects Day-After-Pretraining Perceived Fatigue in Female Volleyball Players

in International Journal of Sports Physiology and Performance
Restricted access

Purchase article

USD  $24.95

Student 1 year online subscription

USD  $112.00

1 year online subscription

USD  $149.00

Student 2 year online subscription

USD  $213.00

2 year online subscription

USD  $284.00

Purpose: The primary aim of this study was to evaluate whether the internal (session rating of perceived exertion [sRPE] and Edwards heart-rate-based method) and external training load (jumps) affect the presession well-being perception on the day after (ie, +22 h), according to age and tactical position, in elite (ie, Serie A2) female volleyball training. Methods: Ten female elite volleyball players (age = 23 [4] y, height = 1.82 [0.04] m, body mass = 73.2 [4.9] kg) had their heart rate monitored during 13 team (115 individual) training sessions (duration: 101 [8] min). Mixed-effect models were applied to evaluate whether sRPE, Edwards method, and jumps were correlated (P ≤ .05) to Hooper index factors (ie, perceived sleep quality/disorders, stress level, fatigue, and delayed-onset muscle soreness) in relation to age and tactical position (ie, hitters, central blockers, opposites, and setters). Results: The results showed a direct relationship between sRPE (P < .001) and presession well-being perception 22 hours apart, whereas the relationship was the inverse for Edwards method internal training load. Age, as well as the performed jumps, did not affect the well-being perception of the day after. Finally, central blockers experienced a higher delayed-onset muscle soreness than hitters (P = .003). Conclusions: Findings indicated that female volleyball players’ internal training load influences the pretraining well-being status on the day after (+ 22 h). Therefore, coaches can benefit from this information to accurately implement periodization in a short-term perspective and to properly adopt recovery strategies in relation to the players’ well-being status.

The authors are with the NeuroMuscular Function Research Group, Dept of Medical Sciences, and Boccia, also the Dept. of Clinical and Biological Sciences, School of Exercise & Sport Sciences (SUISM), University of Turin, Turin, Italy.

Lupo (corrado.lupo@unito.it) is corresponding author.
  • 1.

    Lima RF, Lima RF, Lima RF, et al. . External and internal load and their effects on professional volleyball training. Int J Sports Med. 2020;41(7):468474. PubMed ID: 32059245 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 2.

    McLaren SJ, Macpherson TW, Coutts AJ, Hurst C, Spears IR, Weston M. The relationships between internal and external measures of training load and intensity in team sports: a meta-analysis. Sport Med. 2018;48:641658. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 3.

    García-de-Alcaraz A, Ramírez-Campillo R, Rivera-Rodríguez M, Romero-Moraleda B. Analysis of jump load during a volleyball season in terms of player role. J Sci Med Sport. 2020; 23(10):P973978. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 4.

    Sheppard JM, Gabbett TJ, Stanganelli LCR. An analysis of playing positions in elite men’s volleyball: considerations for competition demands and physiologic characteristics. J Strength Cond Res. 2009;23(6):P18581866. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 5.

    Wnorowski K, Aschenbrenner P, Skrobecki J, Stech M. An assessment of a volleyball player’s loads in a match on the basis of the number and height of jumps measured in real-time conditions. Balt J Heal Phys Act. 2013;5(2):199–206. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 6.

    Lupo C, Capranica L, Tessitore A. The validity of the session-RPE method for quantifying training load in water polo. Int J Sports Physiol Perform. 2014;9(4):656660. PubMed ID: 24231176 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 7.

    Lupo C, Tessitore A, Gasperi L, Gomez MAR. Session-RPE for quantifying the load of different youth basketball training sessions. Biol Sport. 2017;34:1117. PubMed ID: 28416891 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 8.

    Foster C, Florhaug JA, Franklin J, et al. . A new approach to monitoring exercise training/Une Nouvelle approche pour conduire l’entrainement. J Strength Cond Res. 2001;15(1):109115. PubMed ID: 11708692

    • Search Google Scholar
    • Export Citation
  • 9.

    Foster C, Hector LL, Welsh R, Schrager M, Green MA, Snyder AC. Effects of specific versus cross-training on running performance. Eur J Appl Physiol Occup Physiol. 1995;70:367372. PubMed ID: 7649149 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 10.

    Edwards S. The heart rate monitor book. Med Sci Sport Exerc. 1994;26(5):647. doi:

  • 11.

    Lupo C, Ungureanu AN, Frati R, Panichi M, Grillo S, Brustio PR. Player session rating of perceived exertion: a more valid tool than coaches’ ratings to monitor internal training load in elite youth female basketball. Int J Sports Physiol Perform. 2019;15(4):548553. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 12.

    Lupo C, Ungureanu AN, Brustio PR. Session-RPE is a valuable internal load evaluation method in beach volleyball for both genders, elite and amateur players, conditioning and technical sessions, but limited for tactical training and games. Kinesiology. 2020;52(1):3038.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 13.

    de Freitas VH, Nakamura FY, Pereira LA, de Andrade FC, Coimbra DR, Bara Filho MG. Pre-competitive physical training and markers of performance, stress and recovery in young volleyball athletes. Rev Bras Cineantropometria e Desempenho Hum. 2014;17(1):2015. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 14.

    Aoki MS, Arruda AFS, Freitas CG, et al. . Monitoring training loads, mood states, and jump performance over two periodized training mesocycles in elite young volleyball players. Int J Sport Sci Coach. 2017;12(1):130137. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 15.

    Mendes B, Palao JM, Silvério A, et al. . Daily and weekly training load and wellness status in preparatory, regular and congested weeks: a season-long study in elite volleyball players. Res Sport Med. 2018;26(4):462473. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 16.

    Saw AE, Main LC, Gastin PB. Monitoring the athlete training response: subjective self-reported measures trump commonly used objective measures: a systematic review. Br J Sports Med. 2016;50(5):281291. PubMed ID: 26423706 doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 17.

    Gallo TF, Cormack SJ, Gabbett TJ, Lorenzen CH. Pre-training perceived wellness impacts training output in Australian football players. J Sports Sci. 2016;34(15):14451451. PubMed ID: 26637525 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 18.

    Clemente FM, Mendes B, Nikolaidis PT, Calvete F, Carriço S, Owen AL. Internal training load and its longitudinal relationship with seasonal player wellness in elite professional soccer. Physiol Behav. 2017;179(1):262267. doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 19.

    Ungureanu AN, Brustio PR, Boccia G, Rainoldi A, Lupo C. Effects of presession well-being perception on internal training load in female volleyball players. Int J Sports Physiol Perform. 2021;16(5):622–627. doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 20.

    Clemente FM, Silva AF, Clark CCT, et al. . Analyzing the seasonal changes and relationships in training load and wellness in elite volleyball players. Int J Sports Physiol Perform. 2020;15(5):731740. PubMed ID: 32015214 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 21.

    Debien PB, Mancini M, Coimbra DR, De Freitas DGS, Miranda R, Bara Filho MG. Monitoring training load, recovery, and performance of brazilian professional volleyball players during a season. Int J Sports Physiol Perform. 2018;13(9):11821189. PubMed ID: 29584530 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 22.

    Freitas VH, Nakamura FY, Miloski B, Samulski D, Bara-Filho MG. Sensitivity of physiological and psychological markers to training load intensification in volleyball players. J Sport Sci Med. 2014;13(3):571.

    • Search Google Scholar
    • Export Citation
  • 23.

    Dipla K, Tsirini T, Zafeiridis A, et al. . Fatigue resistance during high-intensity intermittent exercise from childhood to adulthood in males and females. Eur J Appl Physiol. 2009;106:645653. PubMed ID: 19404672 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 24.

    Ratel S, Williams CA, Oliver J, Armstrong N. Effects of age and mode of exercise on power output profiles during repeated sprints. Eur J Appl Physiol. 2004;94:204210. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 25.

    Lidor R, Ziv G. Physical and physiological attributes of female volleyball players—a review. J Strength Cond Res. 2010;24(7):19631973. PubMed ID: 20543736 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 26.

    Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation coefficients for reliability research. J Chiropr Med. 2016;15(2):155163. PubMed ID: 27330520 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 27.

    Hooper SL, Mackinnon LT. Monitoring overtraining in athletes: recommendations. Sport Med. 1995;20:321327. doi:

  • 28.

    Hopkins WG. A new view of statistics: a scale of magnitudes for effect statistics. Sportscience. 2002. https://www.sportsci.org/resource/stats/effectmag.html. Retrieved January 1, 2021.

    • Search Google Scholar
    • Export Citation
  • 29.

    Akinwande MO, Dikko HG, Samson A. Variance inflation factor: as a condition for the inclusion of suppressor variable(s) in regression analysis. Open J Stat. 2015;5(7):754767. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 30.

    Cook RD. Detection of influential observation in linear regression. Technometrics. 1977;19(1):1518. doi:

  • 31.

    Buchheit M. Monitoring training status with HR measures: do all roads lead to Rome? Front Physiol. 2014;5:73. PubMed ID: 24578692 doi:

  • 32.

    Faff J, Sitkowski D, Ładyga M, Klusiewicz A, Borkowski L, Starczewska-Czapowska J. Maximal heart rate in athletes. Biol Sport. 2007;24(2):129.

    • Search Google Scholar
    • Export Citation
  • 33.

    Scanlan AT, Fox JL, Poole JL, et al. . A comparison of traditional and modified Summated-Heart-Rate-Zones models to measure internal training load in basketball players. Meas Phys Educ Exerc Sci. 2018;22(4):303309. doi:

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 34.

    Howatson G, Hoad M, Goodall S, Tallent J, Bell PG, French DN. Exercise-induced muscle damage is reduced in resistance-trained males by branched chain amino acids: a randomized, double-blind, placebo controlled study. J Int Soc Sports Nutr. 2012;9:20. PubMed ID: 22569039 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 35.

    Dupuy O, Douzi W, Theurot D, Bosquet L, Dugué B. An evidence-based approach for choosing post-exercise recovery techniques to reduce markers of muscle damage, soreness, fatigue, and inflammation: a systematic review with meta-analysis. Front Physiol. 2018;9:403. PubMed ID: 29755363 doi:

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 441 441 82
Full Text Views 10 10 2
PDF Downloads 9 9 2