Predictors and covariates
depression
REGRESSION
Predictor variables were measured at baseline and after one month and were used to predict smoking cessation or relapse and cigarette consumption after six months. Covariates were measured at six months to examine whether they were associated with smoking status and cigarette consumption at six months. Only predictor variables were used in the machine learning models, but both predictors and covariates were used in the multinomial logistic regression models. At the beginning of the study, the following predictor variables were collected: country of residence, age, gender, a 2-item screening test for depression (yes, no) [Predictors measured at one month included smoking status (no tobacco use in the past 7 days and in the past 4 weeks), number of cigarettes smoked per day, minutes to first cigarette of the day, quit attempts after entering the study (yes/no), intention to quit smoking (3-point response scale), use of nicotine medication, e-cigarettes or heated tobacco after entering the study (yes, no), self-reported use of a smoking cessation app in the past month (yes, no) and perceived usefulness of the study app (6-point scale).Covariates and outcomes measured at six months included the same variables measured at one month, plus three variables automatically collected by the app from all participants: the number of different days the app was accessed, the number of times the app was opened, and the duration of app use, i.e. the interval in days between the first and last day participants used the app.For predictors, we did not include smoking status and cigarettes/day after one month, and for covariates, we did not include cigarettes/day after six months, as these variables captured most of the variance as they were very similar to the outcome variables.