Monitoring progress of athletes is an essential component of the training process. Collegiate distance running coaches often use field tests to assess progress because of a lack of time and resources to do laboratory testing. The purpose of this dissertation was to examine the relationships between physiological and strength variables measured in a laboratory and field testing measures in collegiate distance runners. Collegiate distance runners completed a series of tests in the Sport Science laboratory at East Tennessee State University to obtain physiological and strength parameters, such as V̇ O2max and vertical jump height. The athletes then completed one of two field tests (either a 3 km time trial or a 3 minute all out run test). There were strong correlations between the laboratory measures and the field test performances. These results indicate that strength is an important factor in run performance. Also, a 3 km time trial and a 3 minute all out run test are suitable for athlete monitoring.

No description available.

Bayesian surface smoothing using splines usually proceeds by choosing the smoothness parameter through the use of data driven methods like generalized cross validation. In this methodology, knots of the splines are assumed to lie at the data locations. When anisotropy is present in the data, modeling is done via parametric functions. In the present thesis, we have proposed a non-parametric approach to Bayesian surface smoothing in the presence of anisotropy. We use eigenfunctions generated by thin-plate splines as our basis functions. Using eigenfunctions does away with having to place knots arbitrarily, as is done customarily. The smoothing parameter, the anisotropy matrix, and other parameters are simultaneously updated by a Reversible Jump Markov Chain Monte Carlo (RJMCMC) sampler. Unique in our implementation is model selection, which is again done concurrently with the parameter updates. Since the posterior distribution of the coefficients of the basis functions for any given model order is available in closed form, we are able to simplify the sampling algorithm in the model selection step. This also helps us in isolating the parameters which influence the model selection step. We investigate the relationship between the number of basis functions used in the model and the smoothness parameter and find that there is a delicate balance which exists between the two. Higher values of the smoothness parameter correspond to more number of basis functions being selected. Use of a non-parametric approach to Bayesian surface smoothing provides for more modeling flexibility. We are not constrained by the shape defined by a parametric shape of the covariance as used by earlier methods. A Bayesian approach also allows us to include the results obtained from previous analysis of the same data, if any, as prior information. It also allows us to evaluate pointwise estimates of variability of the fitted surface. We believe that our research also poses many questions for future research.

Abstract available in the Medicine and Sciences in Sports and Exercise.

Abstract available in the Journal of Strength and Conditioning Research.

No description available.

Abstract available in the Medicine and Sciences in Sports and Exercise.

Complex training is commonly utilized in an attempt to facilitate peak power production and Rate of Force Development (RFD) via Post Activation Potentiation (PAP). This research compared the effects of six weeks of; resistance (RT), plyometric (PT), and complex training (CT) upon the relationship between peak ground reaction forces (pGRF), jump performance, and strength. Thirty-four recreationally trained college aged males (21.44 ± 3.32 years) were trained using 1 of 3 methods; resistance (RT; n = 13; 180.14 ± 4.75 cm; 83.85 ± 20.54 kg), plyometric (PT; n = 11; 181.41 ± 7.97 cm; 81.25 ± 10.43 kg), or complex training (CT; n = 10; 185.17 ± 5.56 cm; 87.54 ± 9.04 kg) twice a week for 6 weeks. Participants were tested pre (W1) and post (W9) training to assess vertical jump height (CMVJ; cm), broad jump distance (BDJ; cm), pGRF (N), and multiple 1RM (kg) strength measures. Correlation matrices calculated on % change scores for performance measures from W1 to W9 between conditions, revealed significant positive and negative correlations (r range = -.730 through .996; p < .01* and p < .05**). The greatest total of significant correlation coefficients (r) for % change was seen for the RT group. Differences in training adaptations between groups at both the muscular and neuromuscular level may account for the differences seen.

The purposes of this study were to examine the effectiveness of an athlete monitoring program in managing athlete neuromuscular fatigue across a men's collegiate soccer season as measured by changes in squat jump (SJ) height and to compare possible changes with the session rating of perceived exertion (sRPE) training load (TL). Eighteen outfield Division I men's college soccer players performed SJ testing before each game of the fall season in addition to a baseline measurement at the start of preseason. The athletes provided sRPE values after all training sessions, weight-training, and games. Linear mixed modeling was used to compare changes in SJ height across the season with the baseline, and a correlation coefficient and single-lag cross-correlation coefficient were calculated between TL and changes in SJ height. No statistically significant decreases in SJ height occurred across the season, although a moderate practical decline occurred after the preseason (−1.6 cm, effect size = −0.70). The correlation between TL and changes in SJ height was statistically nonsignificant, whereas the cross-correlation was significant (r = 0.18, p = 0.48 and r = 0.55, p = 0.02, respectively). The athlete monitoring program was successful in managing the athletes' neuromuscular fatigue across the season as evidenced by the maintenance of SJ height and positive relationship between TL and changes in SJ height. Thus, SJ monitoring may serve as a useful fatigue monitoring tool for collegiate soccer athletes. Future study is needed, relating changes in vertical jump performance to other markers of athlete preparedness and performance.

Abstract available in the Annual Coaches and Sport Science College.