Study outcomes and statistical analyses
REGRESSION, RECRUITMENT
The material costs, feasibility and yield of used recruitment strategies were assessed via descriptive statistics. Descriptive statistics report the total material cost (Euros) and total time spent (hours) by research staff for each applied recruitment strategy. In addition, the number and percentage (Participant baseline characteristics and baseline measurements on trial outcomes are presented for the total sample. Continuous variables are described by the mean and standard deviation (SD) for normally distributed variables or by the median and interquartile range (IQR) for non-normally distributed variables. Categorical variables are presented by their number and percentage. Participant characteristics were explored stratified by supermarket location for visual inspection of potential differences in participant characteristics. Considering the physical activity intervention was randomised and implemented at the individual level, baseline characteristics and step count data for the control versus intervention participants of the physical activity coaching app intervention are presented in a separate baseline table.To explore potential sociodemographic differences (age, sex, and educational attainment) within the top five most effective recruitment strategies, we assessed associations between the recruitment strategies (independent variables) and the sociodemographic variables (dependent variables) using linear multilevel models for age and logistic multilevel models for sex and educational attainment. All models included a random intercept on the supermarket level. Males were used as reference category in the analyses of sex, and the combination of low and medium educational attainment was used as reference category for the analyses of educational attainment. Participants with low and medium education were combined in one reference category due to very low participant numbers in the low educated group (< 10 participants for five out of the ten investigated outcomes). Moreover, we provided insight into the feasibility and costs of at-home measurement of cardiometabolic risk by reporting the related costs in terms of travel costs and staff hours for home visiting. Travel costs (Euros) and staff hours were calculated as the sum of all costs and staff hours related to the home visits. Furthermore, descriptive statistics are presented for the number and percentage (We presented regression coefficients and 95% confidence intervals to demonstrate the variation in recruitment effectiveness and at-home measurements across sociodemographic subgroups. We do not describe whether associations were statistically significant, because of the low numbers in some of the analysed subgroups. All analyses were conducted in R (version 4.0.3) using the packages 