BACKGROUND AND AIM[|]Although migraine affects over 1 billion people worldwide, little is known about its environmental triggers. While some research has observed an association between ambient air pollution and migraine events, most studies have been limited to single pollutants and/or relied on emergency-department visit data. Our objective is to estimate the associations between air pollution and migraine events captured using a smartphone app in the province of Ontario, Canada.[¤]METHOD[|]Migraine Buddy is a well-established smartphone app with approximately 3 million users worldwide. Users have an option to share their data, which provides a wealth of individual-level, longitudinal, repeated-event data. Previous work has used statistical methods with pooled rather than individual data. The case time series is a newly developed modeling technique that harnesses longitudinal individual-level data in relation to multiple environmental exposures. Environment and Climate Change Canada has provided 10 km² grids of estimated daily NO₂, PM₂.₅, O₃, and SO₂ and weather variables for 2017-2019. Associations between air pollutants and migraine events will be estimated using the case time series method, accounting for demographic and meteorologic covariates.[¤]RESULTS[|]There were 69,808 migraine attacks reported by 7,447 research-consenting Migraine Buddy users. Interquartile ranges were 7.48 ppb for NO₂, 7.21 μg/m3 for PM₂.₅, 12.74 ppb for O₃, and 1.77 ppb for SO₂. Pearson correlation coefficients with NO₂ were 0.73 for PM₂.₅, -0.42 for O₃, and 0.51 for SO₂.[¤]CONCLUSIONS[|]Results of this study will inform the understanding of associations between air pollution and migraine, as well as the feasibility of smartphone app data for recording health events in epidemiologic research. We expect the richness of the app data and the analytic strength of the case time series method to yield new insights into the effects of multiple air pollutants on migraine. Funding: University of Toronto Data Science Institute Doctoral Fellowship[¤]