Ecological Momentary Assessment

Ecological Momentary Assessment (EMA) is a methodology for collecting real-time data on psychological states and behaviors, aiming to understand dynamic processes like stress and emotion in their natural context. Current research emphasizes improving the accuracy and interpretability of EMA data analysis, focusing on machine learning techniques such as recurrent neural networks, graph neural networks, and ensemble methods to model complex temporal relationships and individual differences. These advancements are improving the ability to predict emotional states, cluster individuals based on behavioral patterns, and personalize interventions, ultimately leading to more effective and targeted mental health support.