Health research has an increasing focus on promoting well-being and positive mental health, to prevent disease and to more effectively treat disorders. The availability of rich multi-modal datasets and advances in machine learning methods are now enabling data science research to begin to objectively assess well-being. However, most existing studies focus on detecting the current state or predicting the future state of well-being using stand-alone health behaviors. There is a need for methods that can handle a complex combination of health behaviors, as arise in real-world data.
In this paper, we present a framework to 1) map multi-modal messy data collected in the “wild” to meaningful feature representations of health behavior, and 2) uncover latent patterns comprising multiple health behaviors that best predict well-being. We show how to use supervised latent Dirichlet allocation (sLDA) 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 self-reported stress, one of the largest components affecting well-being.
We investigate the modifiable behaviors present in these patterns and uncover some ways in which the factors work together to influence well-being. This work contributes a new method using objective data analysis to help individuals monitor their well-being using real-world measurements. Insights from this study advance scientific knowledge on how combinations of daily modifiable human behaviors relate to human well-being.