This study investigated the effects of instantaneous performance feedback during the jumpsquat exercise over a 6-week training period. Twenty-five strength-trained athletes were randomly divided into an instant feedback (n = 13, half-squat 3-RM/body weight = 2.38 ± 0.19) or a non-feedback (n = 12, half-squat 3-RM/body weight = 2.03 ± 0.44) group. Both groups performed the same training program (3×week), consisting of 4 sets of 8 repetitions (weeks 1-3) and 8 sets of 4 repetitions (weeks 4-6) using a barbell with a load that maximized the average concentric power output (Pmax) of each athlete. Subjects in the instant feedback group were given real-time data after each repetition. Pre-, mid-, and post-training testing consisted of maximum 20m, 30m and 50m running speed, 3-RM back half-squat load, Pmax and the load that maximized average concentric power output (Pmax load), countermovement (CMJ) and squat jump (SJ) height. Results revealed that the feedback group significantly improved all selected tests versus non-feedback (time×group interaction, p<0.01). Significant improvements post-training for 20m, 30m, 50m, 3-RM load, Pmax load, CMJ and SJ were observed in the feedback group only (p<0.01). Training without instant feedback did not lead to significant performance improvements, this group actually demonstrated significant decreases in SJ and Pmax (W) and Pmax load (p<0.05). The results of this study indicate that the use of instant feedback during jump-squat training in athletes was beneficial for improving multiple performance tasks over 6-weeks of training. Instant feedback is an important element of power training to maximize adaptations when training strength-trained athletes.

Abstract. The aim of this paper is to provide empirical evidence for the statement that the constraints imposed on an objective function are able to reduce the entropy of the corresponding distributions produced by entropy-maximizing models. This idea is evaluated via an application to an entropy-maximizing spatial interaction model, as a typical representative of the family of entropy-maximizing models used in geography. Eleven versions of this spatial interaction model are fitted separately to six sets of data concerning interregional migration in Slovakia. For each model, the predicted flow distribution is derived, prior to calculation of the corresponding predicted entropy, and then comparison of the entropy values relating to all the models. The results obtained indicate very clearly that constraints imposed on an objective function reduce the initial maximum entropy successively, with this reduction depending on the number and nature of the constraints incorporated.