Abstract
Abstract:
 Interaction, or effect modification, is vital in understanding complex relationships between risk factors and health outcomes. While often studied between two variables, multiple variables actually interact simultaneously, shaping health events. However, detecting these interactions is challenging due to sample size, dataset, and software limitations, leading to incomplete predictions in research.
 To bridge this gap, large-scale studies considering multiple health variables and advanced software are needed. Uncovering interactions can greatly improve personalized medicine and interventions. Temporal aspects matter too, as interaction effects change over time. Longitudinal studies tracking changes offer insights into dynamic interactions.
 New data collection methods like wearables offer real-time exposure data. Merging these with traditional designs enhances interaction detection. Embracing temporal dimensions and novel data methods can yield a nuanced understanding of effective interventions, better clinical choices, and improved health outcomes.
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