Towards the Understanding of Receptivity and Affect in EMAs using Physiological based Machine Learning Method: Analysis of Receptivity and Affect

As mobile health (mHealth) studies become increasingly productive due to the advancements in wearable and mobile sensor technology, our ability to monitor and model human behavior will be constrained by participant receptivity. The reliance on subjective responses for health constructs poses challenges, especially in populations with lower receptivity rates. Researchers have proposed machine-learning approaches to optimize survey timing and delivery to address this. However, there are concerns regarding potential biases or unintended influences on participant responses. Our study explore factors impacting receptivity to ecological momentary assessments (EMA) in a 10-day mHealth study, exploring physiological relationships indicative of receptivity and affect. Utilizing data from 45 participants with wearable devices measuring various biometrics, we employ unsupervised (k-means clustering) and supervised (Random Forest and Neural Networks) machine learning methods to infer affect during non-responses. Findings reveal that triggering EMAs based on a receptivity model reduces reported negative affect by over 3 points (0.29 standard deviations). The predicted affect during non-responses exhibits a bimodal distribution, suggesting more frequent initiation during states of higher positive emotions. The study underscores a clear relationship between affect and receptivity, impacting mHealth study efficacy, especially those using machine learning for EMA triggering. Therefore, we propose a smart trigger that promotes EMA receptivity without influencing affect during sampled time points as future work.