Descriptive statistics; means (SD) or frequency (%), were used to describe the data. Differences between PrMW and PMW were compared using independent sample t test. Correlation between variables and the RSMI, HGS and GS were evaluated with Pearson correlation (r). The variables which showed significant correlations were entered into a multiple regression model in both “enter” and “stepwise” manner to detect significant factors associated with RSMI, HGS and GS. Correlations and regression analyses were done for PrMW and PMW, seperately. The collinearity between variables were verified by the variance inflation factor (VIF) and tolerance (T) values. Thus, VIF values < 10 and T values above 0.1 were considered as acceptable. Data were analysed using SPSS 20.0 and p value < 0.05 was considered statistically significant.

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