Toward Assessing and Recommending Combinations of Behaviors for Improving Health and Well-Being

Abstract

Multiple behaviors typically work together to influence health, making it hard to understand how one behavior might compensate for another. Rich multi-modal datasets from mobile sensors and advances in machine learning are today enabling new kinds of associations to be made between combinations of behaviors objectively assessed from daily life and self-reported levels of stress, mood, and health. In this article, we present a framework to (1) map multi-modal messy data collected in the "wild"to meaningful feature representations of health-related behaviors, (2) uncover latent patterns comprising combinations of behaviors that best predict health and well-being, and (3) use these learned patterns to make evidence-based recommendations that may improve health and well-being. We show how to use supervised latent Dirichlet allocation to model the observed behaviors, and we apply variational inference to uncover the latent patterns. Implementing and evaluating the model on 5,397 days of data from a group of 244 college students, we find that these latent patterns are indeed predictive of daily self-reported levels of stressed-calm, sad-happy, and sick-healthy states. We investigate the patterns of modifiable behaviors present on different days and uncover several ways in which they relate to stress, mood, and health. This work contributes a new method using objective data analysis to help advance understanding of how combinations of modifiable human behaviors may promote human health and well-being.