Predictors of vaping relapse based on e-cigarette use measures: A secondary analysis of longitudinal data from the 'Stop Vaping Challenge' cessation application.

INTRODUCTION: There is a lack of standardized, validated measures for assessing electronic cigarette use, which limits comparability across studies and hinders the development of consistent guidelines. We aimed to evaluate and compare the predictive validity of different e-cigarette use measures in forecasting vaping relapse using a longitudinal dataset from a smartphone cessation app. METHODS: This is a secondary analysis of longitudinal data (2021-2025) from the 'Stop Vaping Challenge' cessation application (Ontario Tobacco Research Unit, University of Toronto; launched in 2021). Eligible participants for this study were adults aged >16 years who reported current e-cigarette use and completed >1 quit challenge lasting for ≥5 minutes with mood and cravings ratings recorded. We assessed five baseline e-cigarette usage indicators (past-month vaping frequency, vaping spending, puffs per session, average e-liquid vaped per week, and pod depletion time) against the primary outcome: duration of abstinence. Kaplan-Meier and Cox-proportional Hazard models were used to measure relapse probability, with AIC and BIC guiding predictor selection. RESULTS: Of the 360 participants, the majority were women (49%), heterosexual (69%), and White (71%). Seventy-eight percent of the challenges (n=364 of 468) relapsed by day 2. Higher past-month vaping frequency (AHR=1.02; 95% CI: 1.00-1.04), taking >9 puffs per session (AHR=1.51; 95% CI: 1.10-1.93) and shorter pod depletion time (AHR=0.96; 95% CI: 0.93-0.99) were associated with relapse in sociodemographic models. However, in the fully adjusted model including all vaping measures and sociodemographic variables, only past-month vaping frequency remained independently associated with relapse (AHR=1.02; 95% CI: 1.00-1.04). CONCLUSIONS: Past-month vaping frequency was the strongest predictor of future vaping relapse. Other behavioral measures, such as number of puffs per session and pod depletion time, contributed to model fit; their effects were not independent when all vaping measures were included simultaneously in the same model.