Impact of two different body mass management strategies on repeat rowing performance

Purpose: The present study was conducted to examine the impact of acute weight loss on repeat 2000-m rowing ergometer performance during a simulated multiday regatta. and to compare two different body mass management strategies between races. Methods: Competitive rowers (N = 16) were assigned to either a control (CON), partial recovery (RECpartial), or complete recovery (RECcomplete) group. Volunteers completed four trials, each separated by 48 h. No weight restrictions were imposed for the first trial. Thereafter, athletes in RECpartial and RECcomplete were required to reduce their body mass by 4% in the 24 h before trial 2, again reaching this body mass before the final two trials. No weight restrictions were imposed on CON. Aggressive nutritional recovery strategies were used in the 2 h following weigh-in for all athletes. These strategies were maintained for the 12-16 h following racing for RECcomplete with the aim of restoring at least three quarters of the original 4% body mass loss. Postrace recovery strategies were less aggressive in RECpartial; volunteers were encouraged to restore no more than half of their initial 4% body mass loss. Results: Acute weight loss increased time to complete the first at-weight performance trial by a small margin (mean 3.0, 95% CI -0.3 to 6.3 s, P = 0.07) when compared with the CON response. This effect decreased when sustained for several day,. Aggressive postrace recovery strategies tended to eliminate the effect of acute Weight loss on subsequent performance. Conclusion: Acute weight loss resulted in a small performance compromise that was reduced or eliminated when repeated over several days. Athletes should be encouraged to maximize recovery in the 12-16 h following racing when attempting to optimize subsequent performance.

Acute weight loss followed by an aggressive nutritional recovery strategy has little impact on on-water rowing performance

Objectives: To assess the influence of moderate, acute weight loss on on-water rowing performance when aggressive nutritional recovery strategies were used in the two hours between weigh in and racing. Methods: Competitive rowers (n=17) undertook three on-water 1800 m time trials under cool conditions ( mean (SD) temperature 8.4 (2.0)degrees C), each separated by 48 hours. No weight limit was imposed for the first time trial-that is, unrestricted body mass (UNR1). However, one of the remaining two trials followed a 4% loss in body mass in the previous 24 hours (WT-4%). No weight limit was imposed for the other trial (UNR2). Aggressive nutritional recovery strategies (WT-4%, 2.3 g/kg carbohydrate, 34 mg/kg Na+, and 28.4 ml/kg fluid; UNR, ad libitum) were used in the first 90 minutes of the two hours between weigh in and performance trials. Results: WT-4% had only a small and statistically non-significant effect on the on-water time trial performance ( mean 1.0 second, 95% confidence interval (CI) 20.9 to 2.8; p=0.29) compared with UNR. This was despite a significant decrease in plasma volume at the time of weigh in for WT-4% compared with UNR (-9.2%, 95% CI -12.8% to -5.6%; p

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.

An empirical study of Hoelfding Racing for model selction in K-nearest neighbor classification

Racing algorithms have recently been proposed as a general-purpose method for performing model selection in machine teaming algorithms. In this paper, we present an empirical study of the Hoeffding racing algorithm for selecting the k parameter in a simple k-nearest neighbor classifier. Fifteen widely-used classification datasets from UCI are used and experiments conducted across different confidence levels for racing. The results reveal a significant amount of sensitivity of the k-nn classifier to its model parameter value. The Hoeffding racing algorithm also varies widely in its performance, in terms of the computational savings gained over an exhaustive evaluation. While in some cases the savings gained are quite small, the racing algorithm proved to be highly robust to the possibility of erroneously eliminating the optimal models. All results were strongly dependent on the datasets used.