Supplementary Material for: Predicting the users’ level of engagement with a smartphone application for smoking cessation: Randomized trial and machine learning analysis

Introduction: Studies of the users’ engagement with smoking cessation application (apps) can help understand how these apps are used by smokers, in order to improve their reach and efficacy. Objective: The present study aimed at identifying the best predictors of the users’ level of engagement with a smartphone app for smoking cessation, and at examining the relationships between predictors and outcomes related to the users’ level of engagement with the app. Methods: A secondary analysis of data from a randomized trial testing the efficacy of the Stop-Tabac smartphone app was used. The experimental group used the “full” app and the control group a “dressed down” app. The study included a baseline, one-month, and six-month follow-up questionnaires. A total of 5293 participants answered at least the baseline questionnaires; however, were included in the current study only the 1861 participants who answered at least the baseline and the one-month follow-up questionnaire. Predictors were measured at baseline and after one month, and outcomes after 6 months. Data were analyzed using machine learning algorithms. Results: The best predictors of the outcomes were, in decreasing order of importance: intention to stop smoking, dependence level, perceived helpfulness of the app, having quit smoking after one-month, self-reported usage of the app after one month, belonging to the experimental group (versus control group), age and years of smoking. Most of these predictors were also significantly associated with the participants level of engagement with the app. Conclusions: This information can be used to further target the app to specific groups of users, to develop strategies to enroll more smokers, and to better adapt the app’s content to the users’ needs.