Statistical Methods
depression, comorbidity, breast cancer
SENSITIVITY, BREAST CANCER
Clinicopathologic characteristics were described with absolute and relative frequencies. Continuous (eg, age at diagnosis) and categorical variables (eg, marital status, education, occupation, comorbidity, past episode of depression, and breast cancer treatments) were compared between the 2 groups using the Student To estimate the effect of mobile community engagement, the primary outcome (moderate to severe distress) was analyzed by a multivariate logit model using a panel data set of individual patients’ weekly DT scores. For the logit model, we controlled for age at diagnosis, marital status, education level, occupation, comorbidity, previous depression, and breast cancer treatments. Following the econometric philosophy, even if the variables are not significant, including all possible variables as control variables can increase the variance of the coefficients but reduce bias. Therefore, with the aim of minimizing bias, we controlled for all possible variables. The dependent variable was the binary variable indicating whether the patient had a DT score higher than or equal to 5 in a given week, and the independent variable was mobile community engagement [Sensitivity analyses were conducted with a dependent variable of DT scores higher than or equal to 3 (mild to severe distress) instead of 5 and panel data sets that were constructed for the total observation period of the past 4 weeks instead of the total 24 weeks. Although the study period was relatively short (6 months), in order to examine the prolonged effects of the mobile community on physical activities and mental distress, our analysis incorporated the data from the past 4 weeks. All analyses were conducted using R Software (version 4.2.2, R Foundation for Statistical Computing), and 