UCLA

Mobile technology has become increasingly popular in the past decade through the combination of device portability and advances in network and internet technology. Smart phones, in particular, are at the forefront of these technologies, enabling users to remotely track and further involve themselves in the management of personal health through participatory mobile health applications. Thus far, the majority of participatory applications currently provide users with data visualizations displaying information germane to the user's medical condition, however, there is still a need for in-depth exploratory and inference-based data analysis using advanced statistical methods to maximize the discernment of potential implications carried in these data streams. This paper provides an overview of the structure of participatory data using an asthma management application as an initial platform and discusses several directions for statistical analysis motivated by three usage cases: individuals using the application, creators of the application, and the scientific community. Methods include functional and semi-parametric data analysis, mixed modeling, and clustering methods to model variables representing asthma wellness as a function of subject-specific, population level, and latent spatiotemporal factors. Societal implications are also discussed.