Background
Longitudinal cohort studies are critical for understanding the evolution of health-influencing behaviors, such as e-cigarette use, over time. Optimizing follow-up rates in longitudinal studies is necessary for ensuring high-quality data with sufficient power for analyses. However, achieving high rates of follow-up in web-based longitudinal studies can be challenging, even when monetary incentives are provided.