In longitudinal observational studies, covariate-informed monitoring times are often associated with imbalances in visit frequency across treatment groups. These imbalances, similar to selection bias, may bias estimators of the exposure effect on an outcome when the same covariates associated with the monitoring times are related to the longitudinal outcome being measured. In this work, we review different weighting methods to recover monitoring balance across treatment groups and we propose a new approach based on the cumulative rate of visits. In particular, our new method outperforms existing methods in contexts where patient covariates are only updated and available at visit times and that their gap times between visits are correlated. We demonstrate that the new weighting approach can be used to readjust for selection bias due to covariate-dependent monitoring times when building simple adaptive treatment strategies using longitudinal data.