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’s distribution. Journal of Statistical Software 8/18. Miller L.H. (1956): Table of percentage points of Kolmogorov statistics. J. Amer. Statist. Assoc. 51: 111-121. R Development Core Team (2008): R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org. Rao C.R. (1948): Tests of significance in multivariate analysis. Biometrika 35: 58-79. Royston J.P. (1992): Approximating the Shapiro-Wilk W-test for non-normality. Statistics and Computing 2: 117-119. Shapiro S.S., Wilk M

Abstract

The objective of the study was to assess the level of patients' need for information about the planned gynecologic surgery.

Material and Methods. The number of 173 patients preparing to undergo planned gynecological procedure were qualified for the study. The participation in the survey was entirely voluntary. Each patient was asked to fulfill the survey conducted using the Amsterdam Preoperative Anxiety and Information Scale- APAIS that enables the estimation of the patient’s need for surgery-related information. Furthermore patients’ clinical and demographic data was collected. Results were analyzed using appropriate statistical tools: the Shapiro-Wilk W-test (for distribution of the studied parameters) and the Mann-Whitney U-test (for comparing two independent groups). P value less than 0.05 was considered statistically significant.

Results. It was shown that premenopausal women have a greater need for information about the planned surgery than postmenopausal patients (p<0.05). Patients, who have never been operated, displayed a significantly greater need (p=0.04) for information about their planned surgery in relation to women who have already undergone surgery. The patient’s age, the phase of the menstrual cycle, the education level, the marital status, as well as the preoperative diagnosis and the type of the planned surgery did not affect the level of the preoperative information requirement (p>0.05).

Conclusions. The high level of the need for information about the planned surgery characterizes premenopausal patients and those operated for the first time.

Abstract

Significant factors affecting body composition and consequently professional and amateur bodybuilders’ performance are both training loads and diet.

The aim was to assess dissimilarities in anthropometrical traits and body composition between males practicing bodybuilding professionally and as amateurs, considering their diet and training.

The study comprised 55 athletes, i.e. 29 professionals attending national championships and 26 amateur bodybuilders. All participants underwent anthropometric measurements involving body height, waist, arm and thigh circumferences and skinfolds covering trunk and extremities. The original nutritional behavior questionnaire and a 24-hour survey were used. An electronic scale was used to measure body weight and body composition was analyzed with the BIA method. In statistical analysis, the Shapiro-Wilk (W-test), t-student and Mann-Whitney U test were applied.

An adipose tissue, assessed on the basis of skinfolds was significantly lower in professionals (p<0.05), whereas lower mean values of body fat free mass (FFM) were found in amateur bodybuilders (p<0.01). Diet survey presented differentiation both in the amount of consumed protein in the diet (1.98 g/kg), in its percentage participation in the diet (21.2%) in favor of the professionals (p<0.05). Significant differentiation was between the groups in the amount of consumed fats (p<0.05). In case of resistance trainings time, energy expenditure and number of trainings were higher for professionals (p<0.05).

Bodybuilders feature better developed muscle mass of extremities and a smaller share of percentage of fat mass in body composition in comparison to amateurs. Professional bodybuilders consume proper amount of carbohydrates and fats and significantly higher level of protein, fiber and energy in diet compared to amateur group. In contrary, higher intake of fats is typical for amateur bodybuilders.

vomiting, diarrhoea, dehydration, ascites, oedema, and RFI > 2 indicating renal injury were excluded from the study. Obtained results guided the division of group A into two subgroups: A1, of azotaemic dogs and A2, of non-azotaemic dogs. The results were analysed using the Statistica 13.3 programme (Tibco Software, USA). The Shapiro–Wilk W-test was used for the estimation of normality in the distributions of concentrations of urinary and serum sodium, effective ECF osmolality, urine specific gravity, and mean arterial pressure in groups A and B and subgroups A1 and A2

calculated based on a standard curve for each cytokine with the use of FindGraph software. Concentrations of C-reactive protein (CRP), haptoglobin (Hp), and pig major acute-phase protein (Pig-MAP) were determined by commercial ELISA kits, according to the manufacturers’ recommendations. The selected kits were the Pig C-Reactive Protein ELISA and Pig Haptoglobin ELISA (Life Diagnostics, Inc., USA) and the PigMAP ELISA (Acuvet Biotech S.L., Spain). Statistical analysis Data from all groups were subjected to the Shapiro–Wilk W test of normality and Levene’s test of equal

. Statistical analysis In the statistical analysis, the SPSS 22.0 (Statistical Package for the Social Sciences) was used as a measure of a central tendency to the mean (X) and the standard deviation (SD) was used as a measure of dispersion. The data were submitted to the Kolmogorov-Smirnov Z and Shapiro-Wilk W tests to check normality depending on the sample size of each group. For a comparative analysis, the Student’s t -test or one-factor ANOVA was performed with C Dunnett’s and Mann-Whitney and Kruskal-Wallis tests for variables that did not present a normal or uniform

secondary outcomes of the study included: 1) cardiac function at week 3 defined by left ventricular function and hemodynamic variables (LVEF, LVESD and LVEDD); 2) patients’ quality of life at week 4 determined from the MLHFQ scores. Statistical analysis Patients’ characteristics were tested for normal distribution using the Shapiro-Wilk W-test and the homogeneity of variance was verified by the Levene’s test. Since distributions were normal and variances were homogenous, parametric tests were selected for statistical analysis. The homogeneity of the groups (in terms of

performed with Statistica 9 (StatSoft®). Normal probability plots with standardized residuals were performed for each parameter. The Shapiro–Wilk W test was used to check normal distribution within each group. The Levene test was used to check for homogeneity of variance. The results of DH activity, pH, NH + 4 -N content, and NO - 3 -N content were used for one-way ANOVA (p < 0.05), including Bonferroni post-hoc tests (B-test). The results of DW, SOM, DMSO reduction, AM, and PoMO were further processed with the Mann–Whitney (M-W) U test (p < 0.05) and Kruskal–Wallis (K

( {\frac{{flight{\rm{ }}time}}{2}} \right)^2}$$ where flight time was the time between the takeoff and landing. Statistical Analyses Normal distribution of the variables was tested using the Shapiro-Wilk W-test. A Student’s t- test for dependent samples was performed to compare maximal kicking velocities and CVs obtained with the dominant and non-dominant leg. In order to explore whether the kicking deficit was related to maximal kicking velocity achieved by the dominant or non-dominant leg (i.e. to test whether the players with the highest maximal kick velocity with

were drawn on non-smoothed images to increase the precision. ROI = region of interest Statistical analysis Data distribution was analyzed using the normal probability plot and Shapiro-Wilks W test. Nonnormally distributed data were analyzed with non-parametric tests and normally distributed data were analyzed with parametric tests. Statistical analysis was performed with Statistica 12 (Statsoft, Tulsa, OK, USA) software. A p value < 0.05 after correction for multiple comparisons was regarded as statistically significant. To test for differences between mean DKI