Use of Bioimpedianciometer as Predictor of Mountain Marathon Performance

  • Vicente Javier Clemente-Suarez
  • Pantelis Theodoros Nikolaidis
Education & Training
Part of the following topical collections:
  1. Latest Technology Trends in Health Sciences (TEEM 2016)


This study aimed to examine the relation among body composition, training experience and race time during a mountain marathon. Body composition and training pre-race experience analyses were conducted previous to a mountain marathon in 52 male athletes. A significant correlation between race time and mountain marathon with chronological age, body fat mass, percentage of body fat (BF), level of abdominal obesity, sport experience and daily training volume was revealed. In addition, BF and athlete’s chronological age were negatively associated with race performance. In contrast, the daily training volume was positively associated with mountain marathon time. A regression analysis showed that race time could be predicted (R2 = .948) by the daily training load, sports experience, age, body fat mass, BF and level of abdominal obesity. The comparison between performance groups regarding to body composition and training characteristics showed that the higher performance group was lighter with lower BF, fat mass and level of abdominal obesity, and with more days of training per week compared with the lower performance group (p < .05). Therefore, coaches and fitness trainers working with mountain marathon runners should develop exercise and nutritional strategies to reduce BF and consider increasing mean daily training volume to improve performance.


Body fat Correlation analysis Ultra-endurance mountain performance 


Compliance with Ethical Standards



Conflict of Interest


Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Hoffman, M.D., Lebus, D.K., Ganong, A.C., Casazza, G.A., and Van Loan, M., Body composition of 161-km ultramarathoners. Int J Sport Med. 31:106–109, 2010.CrossRefGoogle Scholar
  2. 2.
    Knechtle, B., Knechtle, P., Schulze, I., and Kohler, G., Upper arm circumference is associated with race performance in ultra-endurance runners. Br J. Sports Med. 42:295–299, 2008.CrossRefPubMedGoogle Scholar
  3. 3.
    Knechtle, B., Duff, B., Welzel, U., and Kohler, G., Body mass and circumference of upper arm are associated with race performance in ultraendurance runners in a multistage race - the Isarrun 2006. Res Q Exerc Sport. 80:262–268, 2009.CrossRefPubMedGoogle Scholar
  4. 4.
    Hagan, R.D., Upton, S.J., Duncan, J.J., and Gettman, L.R., Marathon performance in relation to maximal aerobic power and training indices in female distance runners. Br J. Sports Med. 21:3–7, 1987.CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Arrese, A.L., and Ostariz, E.S., Skinfold thicknesses associated with distance running performance in highly trained runners. J Sports Sci. 24:69–76, 2006.CrossRefPubMedGoogle Scholar
  6. 6.
    Hagan, R.D., Smith, M.G., and Gettman, L.R., Marathon performance in relation to maximal aerobic power and training indices. Med Sci Sports Exerc. 13(3):185–189, 1981.CrossRefPubMedGoogle Scholar
  7. 7.
    Hoffman, M.D., Anthropometric characteristics of ultramarathoners. Int J. Sports Med. 29:808–811, 2008.CrossRefPubMedGoogle Scholar
  8. 8.
    Kenney, W., and Hodgson, J., (1985). Variables predictive of performance in elite middle-distance runners. Brit. J. Sports Med. 19(4):207–209, 1985.Google Scholar
  9. 9.
    Conley, D.L., and Krahenbuhl, G.S., Running economy and distance running performance of highly trained athletes. Med Sci Sports Exerc. 12(5):357–360, 1980.CrossRefPubMedGoogle Scholar
  10. 10.
    Scott, B.K., and Houmard, J.A., Peak running velocity is highly related to distance running performance. Int J. Sports Med. 15:504–507, 1994.CrossRefPubMedGoogle Scholar
  11. 11.
    Bale, P., Bradbury, D., and Colley, E., Anthropometric and training variables related to 10km running performance. Br J. Sports Med. 20:170–173, 1986.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Scrimgeour, A.G., Noakes, T.D., Adams, B., and Myburgh, K., The influence of weekly training distance on fractional utilization of maximum aerobic capacity in marathon and ultra marathon runners. Eur J Appl Physiol. 55:202–209, 1985.CrossRefGoogle Scholar
  13. 13.
    Billat, V.L., Demarle, A., Slawinski, J., et al., Physical and training characteristics of top-class marathon runners. Med Sci Sports Exerc. 33:2089–2097, 2001.CrossRefPubMedGoogle Scholar
  14. 14.
    Knechtle, B., Wirth, A., Knechtle, P., and Rosemann, T., Training volume and personal best time in marathon, not anthropometric parameters, are associated with performance in male 100-km ultrarunners. J Strength Cond Res. 24:604–609, 2010.CrossRefPubMedGoogle Scholar
  15. 15.
    Knechtle, B., Knechtle, P., and Rosemann, T., No correlation of skin-fold thickness and race performance in male ultra-endurance cyclists in a 600 km ultra-cycling marathon. Hum Mov. 10:91–95, 2009.Google Scholar
  16. 16.
    Clemente, V., Modificaciones de parámetros bioquímicos después de una maratón de montaña. Motricidad. Eur J Hum Mov. 27:75–83, 2011.Google Scholar
  17. 17.
    Donadio, C., Halim, A.B., Caprio, F., Grassi, G., Khedr, B., and Mazzantini, M., Single- and multi-frequency bioelectrical impedance analyses to analyse body composition in maintenance haemodialysis patients: Comparison with dual-energy x-ray absorptiometry. Physiol Meas. 29(6):S517–S524, 2008.CrossRefPubMedGoogle Scholar
  18. 18.
    Sharer, K., Siders, W., Johnson, L., and Lukaski, H., Validity of segmental multiple-frequency bioelectrical impedance analysis to estimate body composition of adults across a range of body mass indexes. Nutrition. 25:25–32, 2009.CrossRefGoogle Scholar
  19. 19.
    Lohman, T.G., Roche, A.F., and Martorell, R., Anthropometric standardization reference manual. Human Kinetics Books, Ilinois, 1988.Google Scholar
  20. 20.
    Knechtle, B., Rüst, C., Knechtle, P., Rosemann, T., and Lepers, R., Age-related changes in ultra-triathlon performances. Extrem Physiol Med. 1(5):2–9, 2012.Google Scholar
  21. 21.
    Leyk, D., Erley, O., Ridder, D., Leurs, M., Rüther, T., Wunderlich, M., Sievert, A., Baum, K., and Essfeld, D., Aged-related changes in marathon and half-marathon performances. Int J Sports Med. 28:513–517, 2007.CrossRefPubMedGoogle Scholar
  22. 22.
    Knechtle, B., Wirth, A., Knechtle, P., Zimmerman, J., and Kohler, P., Personal best marathon performance is associated with performance in a 24-h run and not anthropometry or training volume. Br J. Sports Med. 43:836–839, 2009.CrossRefPubMedGoogle Scholar
  23. 23.
    Knechtle, B., Knechtle, P., Rüst, C.A., Rosemann, T., and Lepers, R., Age, training, and previous experience predict race performance in long-distance inline skaters, not anthropometry. Percept Motor Skill. 114(1):141–156, 2012.CrossRefGoogle Scholar
  24. 24.
    Zamparo, P., Capelli, C., and Pendergast, D., Energetics of swimming: A historical perspective. European Journal of Applied Physiology. 111(3):367–378, 2010.CrossRefPubMedGoogle Scholar
  25. 25.
    Navarro, F., Endurance training. Gymnos, Madrid, 1998.Google Scholar
  26. 26.
    Hewson, D.J., and Hopkins, W.G., Specificity of training and its relation to the performance of distance runners. International Journal of Sports Medicine. 17:199–204, 1996.CrossRefPubMedGoogle Scholar
  27. 27.
    Der, V.J., Langfristing verzogeerte Training-seffect durch konzentriertes Krafttraining. Leistungs Sport Germany. 3:41–44, 1984.Google Scholar
  28. 28.
    Knechtle, B., Rosemann, T., Knechtle, P., and Lepers, R., Predictor variables for a 100-km race time in male ultra-marathoners 1, 2. Percept Motor Skill. 111(3):681–693, 2010.CrossRefGoogle Scholar
  29. 29.
    Knechtle, B., Knechtle, P., and Rosemann, T., Race performance in male mountain ultra-marathoners: Anthropometry or training? Percept Motor Skill. 110(3):721–735, 2010.CrossRefGoogle Scholar
  30. 30.
    Clemente, V., and Gonzalez, J., Four weeks of training with different aerobic workload distributions – Effect on aerobic performance. Eur J Sport Sci. 14(1):1–7, 2014.CrossRefGoogle Scholar
  31. 31.
    Clemente-Suárez, V. J., Dalamitros, A. A., & Nikolaidis, P. T. The effect of a short-term training period on physiological parameters and running performance: Intensity distribution versus constant-intensity exercise. J Sports Med Phys Fitness. 2016. Nov 4. [Epub ahead of print]Google Scholar
  32. 32.
    Clemente-Suárez, V.J., Dalamitros, A., Ribeiro, J., Sousa, A., Fernandes, R.J., and Vilas-Boas, J.P., The effects of two different swimming training periodization on physiological parameters at various exercise intensities. Eur J Sport Sci., 2016. doi: 10.1080/17461391.2016.1253775.PubMedGoogle Scholar
  33. 33.
    Clemente-Suárez, V.J., Fernandes, R.J., Arroyo-Toledo, J.J., Figueiredo, P., González-Ravé, J.M., and Vilas-Boas, J.P., Autonomic adaptation after traditional and reverse swimming training periodizations. Ac Physiol Hung. 102(1):105–113, 2015. doi: 10.1556/APhysiol.102.2015.1.11.CrossRefGoogle Scholar
  34. 34.
    Clemente-Suarez, V.J., The importance of intensity in the prescription of health training. Rev Int Cienc Deporte. 11(41):192–195, 2015. doi: 10.5232/ricyde.CrossRefGoogle Scholar
  35. 35.
    Arroyo-Toledo, J.J., Clemente-Suarez, V.J., and González-Rave, J.M., Effects of Traditional and Reverse Periodization on Strength, Body-Composition and Swim Performance. Imperial J Interdisciplinary Res. 2(12), 2016.Google Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Vicente Javier Clemente-Suarez
    • 1
  • Pantelis Theodoros Nikolaidis
    • 2
  1. 1.Applied Psychophysiological Research GroupEuropean University of MadridMadridSpain
  2. 2.Department of Physical and Cultural EducationHellenic Army AcademyAthensGreece

Personalised recommendations