The aim of this study was to assess the relationship between the intra-cyclic variation of the horizontal velocity (dv) and the velocity of the 4 competitive swimming techniques in young swimmers. 45 young swimmers performed a set of maximal 4 × 25 m (freestyle, backstroke, breaststroke and butterfly stroke) swims with in water start. A speed-meter cable was attached to the swimmer's hip. The dv and the swimming velocity were analyzed. Within-subject tests presented significant variations in the dv based on the swimming technique. Post-hoc test revealed significant differences across all pair-wised swimming techniques (P<0.001), except for the comparison between freestyle and backstroke (P=0.98). The dv was higher in the breaststroke, followed by the butterfly, the backstroke and the freestyle. The quadratic models had the best goodness-of-fit and the lower error of estimation for the relationship between the dv and the swimming velocity in all swimming techniques (0.24 ≤ R(2) ≤ 0.51). As a conclusion, there is a non-linear relationship where the increase of swimming velocity leads to a decrease of dv in young competitive swimmers.

New theories about dynamical systems highlight the multi-factorial interplay between determinant factors to achieve higher sports performances, including in swimming. Longitudinal research does provide useful information on the sportsmen's changes and how training help him to excel. These questions may be addressed in one single procedure such as latent growth modeling. The aim of the study was to model a latent growth curve of young swimmers' performance and biomechanics over a season. Fourteen boys (12.33 ± 0.65 years-old) and 16 girls (11.15 ± 0.55 years-old) were evaluated. Performance, stroke frequency, speed fluctuation, arm's propelling efficiency, active drag, active drag coefficient and power to overcome drag were collected in four different moments of the season. Latent growth curve modeling was computed to understand the longitudinal variation of performance (endogenous variables) over the season according to the biomechanics (exogenous variables). Latent growth curve modeling showed a high inter- and intra-subject variability in the performance growth. Gender had a significant effect at the baseline and during the performance growth. In each evaluation moment, different variables had a meaningful effect on performance (M1: Da, β = -0.62; M2: Da, β = -0.53; M3: η(p), β = 0.59; M4: SF, β = -0.57; all P < .001). The models' goodness-of-fit was 1.40 ⩽ χ(2)/df ⩽ 3.74 (good-reasonable). Latent modeling is a comprehensive way to gather insight about young swimmers' performance over time. Different variables were the main responsible for the performance improvement. A gender gap, intra- and inter-subject variability was verified.

Abstract Background: Currently, there is no validated multivariate model to predict probability of obstructive coronary disease in patients with acute chest pain. Objective: To develop and validate a multivariate model to predict coronary artery disease (CAD) based on variables assessed at admission to the coronary care unit (CCU) due to acute chest pain. Methods: A total of 470 patients were studied, 370 utilized as the derivation sample and the subsequent 100 patients as the validation sample. As the reference standard, angiography was required to rule in CAD (stenosis ≥ 70%), while either angiography or a negative noninvasive test could be used to rule it out. As predictors, 13 baseline variables related to medical history, 14 characteristics of chest discomfort, and eight variables from physical examination or laboratory tests were tested. Results: The prevalence of CAD was 48%. By logistic regression, six variables remained independent predictors of CAD: age, male gender, relief with nitrate, signs of heart failure, positive electrocardiogram, and troponin. The area under the curve (AUC) of this final model was 0.80 (95% confidence interval [95%CI] = 0.75 - 0.84) in the derivation sample and 0.86 (95%CI = 0.79 - 0.93) in the validation sample. Hosmer-Lemeshow's test indicated good calibration in both samples (p = 0.98 and p = 0.23, respectively). Compared with a basic model containing electrocardiogram and troponin, the full model provided an AUC increment of 0.07 in both derivation (p = 0.0002) and validation (p = 0.039) samples. Integrated discrimination improvement was 0.09 in both derivation (p < 0.001) and validation (p < 0.0015) samples. Conclusion: A multivariate model was derived and validated as an accurate tool for estimating the pretest probability of CAD in patients with acute chest pain.