Statistical analysis was performed using programs available in the SPSS V.21.0 statistical package (SPSS Inc, Chicago, Illinois, USA). Data are presented as mean±SD or geometric mean with 95% CI as appropriate according to data distribution. Differences among the four groups were tested with a one-way analysis of variance (ANOVA) (continuous variables) or χ2 test (categorical variables). Linear regression analyzes with a stepwise procedure were used to assess the cross-sectional association of markers for adiposity including ABSI, BMI, WC, VFA, SFA, and VFA/SFA (V/S) ratio with baPWV. In addition, the correlation of z-scores for these measures was determined as reported previously.12 (link) The following covariates were incorporated into the analysis: age, gender, duration of diabetes, smoking status, systolic blood pressure, triglycerides, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, HbA1c, urinary albumin excretion, eGFR, the presence of proliferative diabetic retinopathy and the use of insulin, calcium channel blockers, ACE inhibitors, angiotensin receptor blockers, statins, and antiplatelet agents. Differences were considered to be statistically significant at a p value <0.05.
Spss v 21.0 statistical package
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SPSS V.21.0 is a statistical software package developed by IBM. It is designed to analyze and manipulate data. The software provides a range of statistical functions and tools to help users understand and interpret data.
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Lab products found in correlation
4 protocols using spss v 21.0 statistical package
1
Adiposity Markers and Arterial Stiffness
2
Statistical Analysis of Categorical and Continuous Data
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The analyses were conducted using the SPSS v21.0 statistical package (SPSS Inc., Chicago, Illinois, United States). A Chi-square test was used for comparing the categorical variable between the groups. Student's t-test was used for comparing the normally distributed parameters, and Mann–Whitney U-test was used for comparing the non-normally distributed parameters; P < 0.05 was considered statistically significant.
3
Psychological Differences in Sport Modalities
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T-test for independent samples were used to test differences between sport modality (cycling vs. triathlon), gender and level (pro vs. amateur) regarding to the different psychological variables.
In addition to the univariate analyses, the psychological measures of the athletes between sports (cycling and triathlon), gender (men and women), and categories (i.e., professional and amateur) were compared by the method of magnitude-based inferences (MBI) (standardized Cohen’s d values and their 90% Confidence Intervals). The comparison on each psychological measure was done using the Hopkins’ spreadsheet with the smallest worthwhile difference (Batterham and Cox, 2006 ). This method calculated 0.2 times the standardization, estimated from the between-subjects standard deviation. According to the authors (Hopkins et al., 2009 (link)) the differences can be defined as unclear if the confidence intervals for the difference in the means included substantial positive and negative values (± 0.2∗standardization) simultaneously. In order to assess the differences between pairs of comparisons, the magnitude of a clear difference was assessed as follows: >0.25, trivial; 0.25–75% possibly, 75–95% likely, 95–99% very likely, >99% most likely (Hopkins, 2007 ). All data analyses were carried out using the SPSS v. 21.0 Statistical package.
In addition to the univariate analyses, the psychological measures of the athletes between sports (cycling and triathlon), gender (men and women), and categories (i.e., professional and amateur) were compared by the method of magnitude-based inferences (MBI) (standardized Cohen’s d values and their 90% Confidence Intervals). The comparison on each psychological measure was done using the Hopkins’ spreadsheet with the smallest worthwhile difference (Batterham and Cox, 2006 ). This method calculated 0.2 times the standardization, estimated from the between-subjects standard deviation. According to the authors (Hopkins et al., 2009 (link)) the differences can be defined as unclear if the confidence intervals for the difference in the means included substantial positive and negative values (± 0.2∗standardization) simultaneously. In order to assess the differences between pairs of comparisons, the magnitude of a clear difference was assessed as follows: >0.25, trivial; 0.25–75% possibly, 75–95% likely, 95–99% very likely, >99% most likely (Hopkins, 2007 ). All data analyses were carried out using the SPSS v. 21.0 Statistical package.
4
Stroke Mortality Risk Factors
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Baseline and outcome data are presented in frequency and percentages for categorical variables and interval variables are presented in mean and SDs or median and range as appropriate. In order to establish the association between stroke and non-stroke groups χ2 tests were applied. Student t tests or Wilcoxon rank sum tests were used for interval variables between the two groups as appropriate. Multivariate logistic regression analysis was performed at in-hospital and 1 year mortality for important risk factors. Adjusted OR and 95% CI with p values are presented in table 6 . p Value ≤0.05 (two tailed) is considered statistically significant. SPSS V.21.0 Statistical package was used for the analysis.21 (link)
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