Percentile curves and reference values for 2000-m rowing ergometer performance time in international rowers aged 14-70 years
Abstract
The aim of this study was to provide percentile curves and reference values for the performance in 2000-m maximal effort on rowing ergometer. A cross-sectional study was carried out with a non-probabilistic sample (n=15420) obtained from an on-line ranking of indoor rowing and made from results between 2010 and 2014 recorded in 2000-m official races. Percentile curves and reference values were calculated using Generalized Additive Models for Location, Scale and Shape (GAMLSS) with a transformation of data to Box-Cox Power Exponential distribution and cubic splines as smoothing technique with age as the explanatory variable. This study is the first to present percentile curves and reference data to evaluate 2000-m performance time (indirect measure of mechanical power) in rowing ergometer depending on age (14-70) for both sexes and body-mass classifications (light- and heavyweight rowers). These curves and values are of interest in assessing indoor rowing performance and in measuring the specific physical condition of rowers in 2000-m regattas on-water. Percentiles also can be useful to predict performance levels in oncoming ages.
Keywords
References
Akça, F. (2014). Prediction of Rowing Ergometer Performance from Functional Anaerobic Power, Strength and Anthropometric Components. Journal of human kinetics, 41(1), 133-142. https://doi.org/10.2478/hukin-2014-0041
Borghi, E., de Onis, M., Garza, C., Van den Broeck, J., Frongillo, E. A., Grummer-Strawn, L., Van Buuren, S., Pan, H., Molinari, L., Martorell, R., Onyango, A. W. and Martines, J. C. (2006). Construction of the World Health Organization child growth standards: selection of methods for attained growth curves. Statistics in medicine, 25(2), 247-265. https://doi.org/10.1002/sim.2227
Bourdin, M., Messonnier, L., Hager, J. P., & Lacour, J. R. (2004). Peak power output predicts rowing ergometer performance in elite male rowers. International journal of sports medicine, 25(5), 368-373. https://doi.org/10.1055/s-2004-815844
Buuren, S. V. & Fredriks, M. (2001). Worm plot: a simple diagnostic device for modelling growth reference curves. Statistics in medicine, 20(8), 1259-1277. https://doi.org/10.1002/sim.746
Cosgrove, M. J., Wilson, J., Watt, D. & Grant, S. F. (1999). The relationship between selected physiological variables of rowers and rowing performance as determined by a 2000 m ergometer test. Journal of Sports Sciences, 17(11), 845-852. http://dx.doi.org/10.1080/026404199365407
Guével, Boyas, Nordez & Cornu (2006). Power responses of a rowing ergometer: mechanical sensors vs. Concept2® measurement system. Int J Sports Med, 27, 830-833. http://dx.doi.org/10.1055/s-2006-923774
de Campos Mello, F., de Moraes Bertuzzi, R. C., Grangeiro, P. M. & Franchini, E. (2009). Energy systems contributions in 2,000 m race simulation: a comparison among rowing ergometers and water. European journal of applied physiology, 107(5), 615-619. http://dx.doi.org/10.1007/s00421-009-1172-9
Hagerman, F. C. (1984). Applied physiology of rowing. Sports Medicine, 1(4), 303-326. http://dx.doi.org/10.2165/00007256-198401040-00005
Hawkins, S. A. & Wiswell, R. A. (2003). Rate and mechanism of maximal oxygen consumption decline with aging. Sports Medicine, 33(12), 877-888. http://dx.doi.org/10.2165/00007256-200333120-00002
Huang, C. J., Nesser, T. W., & Edwards, J. E. (2007). Strength and power determinants of rowing performance. Journal of Exercise Physiology online, 10(4).
Ingham, S., Whyte, G., Jones, K. & Nevill, A. (2002). Determinants of 2,000 m rowing ergometer performance in elite rowers. European journal of applied physiology, 88(3), 243-246. http://dx.doi.org/10.1007/s00421-002-0699-9
Kerr, D. A., Ross, W. D., Norton, K., Hume, P., Kagawa, M. & Ackland, T. R. (2007). Olympic lightweight and open-class rowers possess distinctive physical and proportionality characteristics. Journal of Sports Sciences, 25(1), 43-53. http://dx.doi.org/10.1080/02640410600812179
Kleshnev, V. (2010). Boat acceleration, temporal structure of the stroke cycle, and effectiveness in rowing. Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 224(1), 63-74. https://doi.org/10.1243/17543371JSET40
Klusiewicz, A. & Faff, J. (2003). Indirect methods of estimating maximal oxygen uptake on the rowing ergometer. Biology of Sport, 20(3), 181-194.
Lacour, J. R., Messonnier, L. & Bourdin, M. (2009). Physiological correlates of performance. Case study of a world-class rower. European journal of applied physiology, 106(3), 407-413. https://doi.org/10.1007/s00421-009-1028-3
Mäestu, J., Jürimäe, J. & Jürimäe, T. (2005). Monitoring of performance and training in rowing. Sports Medicine, 35(7), 597-617. https://doi.org/10.2165/00007256-200535070-00005
Mikulić, P., Smoljanović, T., Bojanić, I., Hannafin, J. A., & Matković, B. R. (2009). Relationship between 2000-m rowing ergometer performance times and World Rowing Championships rankings in elite-standard rowers. Journal of sports sciences, 27(9), 907-913. https://doi.org/10.1080/02640410902911950
Nevill, A. M., Allen, S. V. & Ingham, S. A. (2011). Modelling the determinants of 2000 m rowing ergometer performance: a proportional, curvilinear allometric approach. Scandinavian journal of medicine & science in sports, 21(1), 73-78. http://dx.doi.org/10.1111/j.1600-0838.2009.01025.x
Nybo, L., Schmidt, J. F., Fritzdorf, S. & Nordsborg, N. B. (2014). Physiological characteristics of an aging olympic athlete. Medicine and science in sports and exercise, 46(11), 2132-2138. https://doi.org/10.1249/MSS.0000000000000331
R Core Team (2016). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/
Rigby, R. A., & Stasinopoulos, D. M. (2006). Using the Box-Cox t distribution in GAMLSS to model skewness and kurtosis. Statistical Modelling, 6(3), 209-229. http://doi.org/10.1191/1471082X06st122oa
Rubin, R. T. & Rahe, R. H. (2010). Effects of aging in Masters swimmers: 40-year review and suggestions for optimal health benefits. Open access journal of sports medicine, 1, 39. http://doi.org/10.2147/OAJSM.S37718
Secher, N. H. (1983). The physiology of rowing. Journal of Sports Sciences,1(1), 23-53. http://doi.org/10.1080/02640418308729658
Seiler, K. S., Spirduso, W. W., & Martin, J. C. (1998). Gender differences in rowing performance and power with aging. Medicine and science in sports and exercise, 30(1), 121-127. http://doi.org/10.1097/00005768-199801000-00017
Shephard, R. J. (1998). Science and medicine of rowing: a review. Journal of Sports Sciences, 16(7), 603-620. http://doi.org/10.1080/026404198366416
Smith, T. B. & Hopkins, W. G. (2012). Measures of rowing performance. Sports Medicine, 42(4), 343-358. http://doi.org/10.2165/11597230-000000000-00000
Soper, C. & Hume, P. A. (2004). Towards an ideal rowing technique for performance: The contributions from biomechanics. Sports Medicine, 34(12), 825-848. http://doi.org/10.2165/00007256-200434120-00003
Stasinopoulos, D. M. & Rigby, R. A. (2007). Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, 23(7), 1-46. https://doi.org/10.18637/jss.v023.i07
Steinacker, J. M. (1993). Physiological aspects of training in rowing. Evaluation,47(57), 60-62.
Van Holst, M. (2012). On rowing. URL http://home.hccnet.nl/m.holst/RoeiWeb.html
Vogler, A. J., Rice, A. J. & Withers, R. T. (2007). Physiological responses to exercise on different models of the concept II rowing ergometer. International Journal of Sports Physiology and Performance, 2(4), 360. http://doi.org/10.1123/ijspp.2.4.360
DOI: https://doi.org/10.14198/jhse.2018.134.02
Copyright (c) 2018 Journal of Human Sport and Exercise

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.