Effects of Presession Well-Being Perception on Internal Training Load in Female Volleyball Players

in International Journal of Sports Physiology and Performance

Click name to view affiliation

Alexandru Nicolae Ungureanu
Search for other papers by Alexandru Nicolae Ungureanu in
Current site
Google Scholar
PubMed Close
,
Paolo Riccardo Brustio
Search for other papers by Paolo Riccardo Brustio in
Current site
Google Scholar
PubMed Close
,
Gennaro Boccia
Search for other papers by Gennaro Boccia in
Current site
Google Scholar
PubMed Close
,
Alberto Rainoldi
Search for other papers by Alberto Rainoldi in
Current site
Google Scholar
PubMed Close
, and
Corrado Lupo
Search for other papers by Corrado Lupo in
Current site
Google Scholar
PubMed Close
DOI:
https://doi.org/10.1123/ijspp.2020-0387
Keywords:
women’s volleyball; perceived exertion; team sports; heart-rate monitoring; Hooper index
First Published Online:
28 Jan 2021
In Print:
Volume 16: Issue 5
Page Range:
622–627
Restricted access

Purpose: To evaluate if the internal training load (ITL; Edwards heart rate [HR]-based and session-rating of perceived exertion [RPE] methods) is affected by the presession well-being perception, age, and position in elite (ie, Serie A2) female volleyball training. Methods: Twelve female elite volleyball players (age: 22 [4] y, height: 1.80 [0.06] m, body mass: 74.1 [4.3] kg) were monitored using an HR monitor during 32 team training sessions (duration: 1:36:12 [0:22:24], in h:min:s). Linear mixed-effects models were applied to evaluate if well-being perception (ie, perceived sleep quality/disorders, stress level, fatigue, and delayed-onset muscle soreness) may affect ITL depending on age and tactical position. Results: Presession perceived fatigue influenced ITL according to the session-RPE (P = .032) but not according to the Edwards method. Age was inversely correlated to the Edwards method (P < .001) and directly correlated to the session-RPE (P = .027). Finally, central blockers experienced a higher training load than hitters (P < .001) and liberos (P < .001) for the Edwards method, as well as higher than hitters (P < .001), liberos (P = .003), and setters (P = .008) for  session-RPE. Conclusions: Findings indicated that female volleyball players’ perceived ITL is influenced by presession well-being status, age, and position. Therefore, coaches can benefit from this information to specifically predict players’ ITL in relation to their individual characteristics.

The authors are with the Neuromuscular Function Research Group, School of Exercise & Sport Sciences (SUISM), Dept of Medical Sciences, University of Turin, Turin, Italy.

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

International Journal of Sports Physiology and Performance

Cover International Journal of Sports Physiology and Performance
  • 1.

    Impellizzeri FM, Rampinini E, Coutts AJ, Sassi A, Marcora SM. Use of RPE-based training load in soccer. Med Sci Sports Exerc. 2004;36(6):1042–1047. PubMed ID: 15179175 doi:10.1249/01.MSS.0000128199.23901.2F

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

    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):656–660. PubMed ID: 24231176 doi:10.1123/ijspp.2013-0297

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

    Lupo C, Tessitore A, Gasperi L, Gomez MAR. Session-RPE for quantifying the load of different youth basketball training sessions. Biol Sport. 2017;34(1):11–17. PubMed ID: 28416891 doi:10.5114/biolsport.2017.63381

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

    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):109–115. PubMed ID: 11708692 doi:10.1519/1533-4287(2001)015<0109:ANATME>2.0.CO;2

    • Search Google Scholar
    • Export Citation
  • 5.

    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(4):367–372. PubMed ID: 7649149 doi:10.1007/BF00865035

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

    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. 2020;15(4):548–553. doi:10.1123/ijspp.2019-0248

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

    Govus AD, Coutts A, Duffield R, Murray A, Fullagar H. Relationship between pretraining subjective wellness measures, player load, and rating-of-perceived-exertion training load in American College Football. Int J Sports Physiol Perform. 2018;13(1):95–101. PubMed ID: 28488913 doi:10.1123/ijspp.2016-0714.

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

    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):731–740. PubMed ID: 32015214 doi:10.1123/ijspp.2019-0251

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 9.

    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):30–38.

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

    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. 2015;17(1):31. doi:10.5007/1980-0037.2015v17n1p31

    • Search Google Scholar
    • Export Citation
  • 11.

    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):130–137. doi:10.1177/1747954116684394

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

    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):1858–1866. PubMed ID: 19675472 doi:10.1519/JSC.0b013e3181b45c6a

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

    Inkinen V, Häyrinen M, Linnamo V. Technical and tactical analysis of women’s volleyball. Biomed Hum Kinet. 2014;5(1):43–50. doi:10.2478/bhk-2013-0007

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

    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 Sports Med. 2018;26(4):462–473. PubMed ID: 29969288 doi:10.1080/15438627.2018.1492393

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

    Robertson RJ. Central signals of perceived exertion during dynamic exercise./Signaux centraux de l’ effort percu lors d’ un exercice dynamique. Med Sci Sport Exerc. 1982;14(5):390–396. doi:10.1249/00005768-198205000-00014

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

    Harmison RJ. Peak performance in sport: identifying ideal performance states and developing athletes’ psychological skills. Prof Psychol Res Pract. 2006;37(3):233–243. doi:10.1037/0735-7028.37.3.233

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

    Mielgo-Ayuso J, Zourdos MC, Clemente-Suárez VJ, Calleja-González J, Shipherd AM. Can psychological well-being scales and hormone levels be used to predict acute performance of anaerobic training tasks in elite female volleyball players? Physiol Behav. 2017;180:31–38. PubMed ID: 28811191 doi:10.1016/j.physbeh.2017.08.008

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

    Hooper SL, Mackinnon LT. Monitoring overtraining in athletes: recommendations. Sport Med. 1995;20(5):321–327. doi:10.2165/00007256-199520050-00003

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

    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;179:262–267. PubMed ID: 28668619 doi:10.1016/j.physbeh.2017.06.021

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

    Haddad M, Chaouachi A, Wong DP, et al. Influence of fatigue, stress, muscle soreness and sleep on perceived exertion during submaximal effort. Physiol Behav. 2013;119:185–189. PubMed ID: 23816982 doi:10.1016/j.physbeh.2013.06.016

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

    EcheverrÍa CJ, Ortega E, Palao JM. Evolution of reception efficacy and execution in women’s volleyball according to level of competition – a descriptive study aged from 14-year-old to adult professional players. Cent Eur J Sport Sci Med. 2019;27(3):55–64. doi:10.18276/cej.2019.3-05

    • Search Google Scholar
    • Export Citation
  • 22.

    Lidor R, Ziv G. Physical and physiological attributes of female volleyball players-a review. J Strength Cond Res. 2010;24(7):1963–1973. PubMed ID: 20543736 doi:10.1519/JSC.0b013e3181ddf835

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
  • 23.

    Edwards S. The heart rate monitor book. Med Sci Sports Exerc. 1994;26(5):647. doi:10.1249/00005768-199405000-00020

  • 24.

    Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2):461–464. doi:10.1214/aos/1176344136

  • 25.

    Batterham AM, Hopkins WG. Making meaningful inferences about magnitudes. Int J Sports Physiol Perform. 2006;1(1):50–57. PubMed ID: 19114737 doi:10.1123/ijspp.1.1.50

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

    R Foundation for Statistical Computing. R: A Language and Environment for Statistical Computing. 2018. Vienna, Austria.

  • 27.

    Bates D, Mächler M, Bolker BM, Walker SC. Fitting linear mixed-effects models using lme4. J Stat Softw. 2015;67(1):1–48. doi:10.18637/jss.v067.i01

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 28.

    Lenth R, Singmann H, Love J, Buerkner P, Herve M. Emmeans: estimated marginal means, aka least-squares means. R Packag version 115-15. 2019 . https://cran.r-project.org/web/packages/emmeans/index.html. Accessed February 12, 2020.

    • Search Google Scholar
    • Export Citation
  • 29.

    Smith DJ, Roberts D, Watson B. Physical, physiological and performance differences between Canadian national team and universiade volleyball players. J Sports Sci. 1992;10(2):131–138. PubMed ID: 1588683 doi:10.1080/02640419208729915

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

    Bara Filho MG, de Andrade FC, Alves Nogueira R, Yuzo Nakamura F. Comparison of different methods of internal load control in volleyball players. Rev Bras Med do Esporte. 2013;19(2):143–146. doi:10.1590/S1517-86922013000200015

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 31.

    Araújo R, Mesquita I, Marcelino R. Relationship between block constraints and set outcome in elite male volleyball. Int J Perform Anal Sport. 2009;9(3):306–313. doi:10.1080/24748668.2009.11868487

    • Crossref
    • Search Google Scholar
    • Export Citation
  • 32.

    Purkhús E, Krustrup P, Mohr M. High-intensity training improves exercise performance in elite women volleyball players during competitive season. J Strength Cond Res. 2016;30(11):3066–3072. PubMed ID: 26950353 doi:10.1519/JSC.0000000000001408

    • Crossref
    • PubMed
    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 2889 902 24
Full Text Views 66 11 0
PDF Downloads 71 14 0