Statistical analysis
weight loss
SECONDARY
The NoHoW project tested a model informed by the NoHoW with two paths: one representing motivational plus self-regulation processes; and another representing emotional and stress regulation processes. The present study focused on the first path, depicted in Fig. We used change scores of the variables in the correlational and mediational analysis. These variables were expressed by the residuals of the 6-month or 12-month value regressed on the baseline score. Using such residualized change scores is recommended as it creates a value orthogonal to the pre-treatment value(s) and represents a preferable measure of change compared with the pre-post subtraction procedure [Secondly, SPSS version 23.0 was used to estimate descriptive statistics (means and standard deviations) and the study variables’ bivariate correlations (Pearson correlation coefficient). Subsequently, the reliability of the subscales was analyzed using the Omega coefficient.Thirdly, Mplus version 8 [Fourthly, the path analysis was re-estimated using a maximum likelihood estimator and bootstrapping resampling procedures (Finally, for our secondary analysis, which hypothesized that the significant associations would differ in sub-groups with different weight change results, we used the same analytical procedures presented above. The sample was categorized according to the percentage of weight loss maintenance attained using known criteria [