Statistical analysis
For proper statistical analyses, a Windows-based SPSS 24.0  statistical analysis program was used (SPSS Inc., USA) . We examined variables via visual (histograms, probability plots) and analytical methods (Shapiro-Wilk’s and Kolmogorov-Smirnov test) to determine whether they were normally distributed or not. Variables specified as mean±standard deviation (X±SD), the mean difference between groups, 95% confidence interval (95%CI), median (minimum-maximum (min-max)), U value, frequency (n), and percentage (%). Student t-test , Mann-Whitney U test, and Chi-square test were used to compare normally distributed, undistributed, and categorical variables. Pearson and Spearman’s tests were conducted to show relationships between normally and non-normally distributed and/or ordinal variables. The level of significance was as p≤0.05. For the multivariate analysis, the possible factors identified with previous analyses were further entered into the logistic regression analysis to determine independent predictors of study outcomes. Hosmer-Lemeshow goodness of fit statistics was for evaluating model fit. A %5 type-1 error level was accepted to infer statistical significance. The diagnostic values of AAWT, HWT, FWT, and TATT measures in predicting labor prolongation, arrest, and cesarean delivery were examined by ROC curve analysis. When a significant cut-off value was observed, the sensitivity, specificity, positive and negative predictive values were presented. While evaluating the area under the curve, a %5 type-1 error level was used to accept a statistically significant test variable’s predictive value.