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MEDICINA DELLO SPORT
A Journal on Sports Medicine
Official Journal of the Italian Sports Medicine Federation
Indexed/Abstracted in: BIOSIS Previews, EMBASE, Science Citation Index Expanded (SciSearch), Scopus
Impact Factor 0,163
FUNCTIONAL EVALUATION SECTION
Medicina dello Sport 2000 September;53(3):247-54
Prediction of rowing performance from selected physiological variables. Differences between lightweight and open class rowers
Maestu J., Jurimae J., Jurimae T.
Institute of Sport Pedagogy, University of Tartu, Tartu, Estonia
Background. The purpose of this study was to investigate the relationships between anthropometric variables, metabolic characteristics and 2500 metre rowing ergometer performance in lightweight and open class sculling rowers. It was hypothesized that a combination of physiological variables would predict performance better than either individual variables or one category of variables in both categories of studied rowers.
Methods. Eight lightweight (182.10±5.83 cm; 73.06±3.90 kg; %body fat: 8.45±1.17%) and 13 open class (189.97±5.22 cm; 89.00±3.85 kg; 12.03±1.95%) rowers were subjected to three measurement sessions on a rowing ergometer. An incremental exercise test to determine the maximal oxygen consumption (V.O2max), the corresponding maximal aerobic power (Pamax) and anaerobic threshold (AT4), a 2500 metre “all-out” test to determine the performance parameters, and five and 20 strokes tests to determine maximal anaerobic alactic (P5) and lactic (P20) power, respectively, were performed.
Results. Significant relationships were obtained between the rowing performance and %body fat, lean body mass, P5, P20, AT4 (W), Pamax and V.O2max values (r=0.71-0.98) in lightweight rowers. Rowing performance was significantly related to the following parameters in open class rowers: body mass, lean body mass, skeletal mass, cross-sectional area of thigh, P5, P20 and Pamax indices (r=0.57-0.74). Multiple regression analyses indicated that the prediction model using the combination of physiological categories predicted rowing performance best (R=0.99), followed by metabolic (R=0.99) and anthropometric (R=0.76) variables in lightweight rowers. In open class rowers, the best prediction model was also a combination of physiological categories (R=0.82), followed by anthropometric (R=0.76) and metabolic (R=0.70) variables.
Conclusions. The results of this study indicate that the prediction model consisting of variables from different physiological categories would predict performance better in sculling rowing than either individual variables or one category of variables in lightweight and open class rowers. However, rowing performance was better characterised by metabolic and anthropometric variables in lightweight and open class rowers, respectively.