Tootfinder

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Zachary D King, Maryam Khalid, Han Yu, Kei Shibuya, Khadija Zanna, Marzieh Majd, Ryan L Brown, Yufei Shen, Thomas Vaessen, George Kypriotakis, Christopher P Fagundes, Akane Sano
https://arxiv.org/abs/2506.15834

Machine Learning-based Context-Aware EMAs: An Offline Feasibility Study
Mobile health (mHealth) systems help researchers monitor and care for patients in real-world settings. Studies utilizing mHealth applications use Ecological Momentary Assessment (EMAs), passive sensing, and contextual features to develop emotion recognition models, which rely on EMA responses as ground truth. Due to this, it is crucial to consider EMA compliance when conducting a successful mHealth study. Utilizing machine learning is one approach that can solve this problem by sending EMAs bas…