2.7. Statistical Analysis
MetS
REGRESSION
Baseline characteristics according to MetS classification were compared using the chi-squared test for categorical variables, the Kruskal–Wallis test for continuous variables and repeated-measures variables (i.e., variables collected daily using the daily questionnaire: daily alcohol intake), or via the wearable device (step count, active minutes, and TST).Associations between MetS status and PA level (step count and active minutes) based on the EPAR reference values were estimated using multilevel mixed-effects logistic regression models by considering 95% confidence intervals (CI) while accounting for goodness of fit. Model 1 included age and sex as fixed effects. Model 2 additionally included the day of the week (weekday or weekend/holiday), and eating habit, smoking, overtime hours, living arrangement, socio-economic status (all fixed effects), alcohol consumption and TST (random effects) as lifestyle factors. Moreover, because participants’ PA may be influenced by health awareness and daily behaviors, we adjusted for health awareness and SRH [The sensitivity analysis investigated the association between MetS status and PA level by stratifying the main analyses according to the day of the week (weekday or weekend/holiday). We investigated the effect modification of the day of the week using the likelihood ratio test by comparing a multivariable model that included the interaction term between the day of the week and MetS with a multivariable model without this term.All statistical analyses were performed using Stata/MP version 16.1 (Stata Corp LLC, College Station, TX, USA). A two-tailed 