Statistical analyses
low chronic stress
REGRESSION, REGRESSIONS
Analyses were based on the 2,220 participants with height and body mass data at the start of the study commencement. For the purposes of this study, participants were divided in four groups separately for both outcome variables (cessation and chronic stress, Fig. Data were checked for missing values. For continuous variables of social support, intentions, QoL, mood, and stress missing values were imputed using automatic method with 5000 case and four parameter draws. For the categorial variable of “primary healthcare utilization” missing values were conservatively recoded as negative values i.e. no healthcare service use or money spent for PA. Data were inspected for outliers and normality. All values were within the expected ranges. Multivariate outliers for continuous variables (QoL, social support, and mood) were examined using Mahalanobis distance with probability of Chi-Square tests were used examine differences in demographic characteristics between the different outcome categories for drop out and chronic stress using standardized residual of ≤  ± 2.24 ≥ as a cut-off point for significance. Three multinomial logistic regressions were performed for both outcome variables; (1) time of cessation with completers as a reference category, and (2) chronic stress with low chronic stress as a reference category. For both outcomes (time of cessation, chronic stress), the first model tested overall significance with age, degree of education, mood, healthcare utilization, QoL, and social support as predictor variables.The second model examined whether variables that emerged as significant predictors in the first model predict intervention cessation and chronic stress independently from BMI. For the second model sequential approach was used. First, a logistic regression (lr_a) was calculated with intervention cessation/chronic stress as the dependent variable and BMI as the predictor variable. Second, a logistic regression (lr_b) was calculated with BMI and significant predictors from the model one. Significance of the The third model tested interaction effects with sex (two categories) and degree of education (five categories) as moderating variables. When analyses indicated moderation but more than one category of the moderator appeared to be significant (e.g. both sexes emerged as potentially significant moderators), the following formula was used to determinate whether there was significant difference between the categories’ regression coefficients with z ≤  ± 2.24 ≥ used as a cut-off point for significant differences. As the variable education had five categories, the lowest significant degree of education was used as a reference category. For the variable sex was dichotomous, no reference category was needed.Due to multiple testing, the Bonferroni adjusted p-value of 