Players across the health ecosystem are initiating studies of thousands, even millions, of participants to gather diverse types of data, including biomedical, behavioral, and lifestyle in order to advance medical research. These efforts to collect multi-modal data sets on large cohorts coincide with the rise of broad activity and behavior tracking across industries, particularly in healthcare and the growing field of mobile health (mHealth). Government and pharmaceutical sponsored, as well as patient-driven group studies in this arena leverage the ability of mobile technology to continuously track behaviors and environmental factors with minimal participant burden. However, the adoption of mHealth has been constrained by the lack of robust solutions for large-scale data collection in free-living conditions and concerns around data quality.
The data collected from commercial mobile health devices can be effectively used to discover meaningful behavioral phenotypes.
In this work, we describe the infrastructure Evidation Health has developed to collect mHealth data from millions of users through hundreds of different mobile devices and apps. Additionally, we provide evidence of the utility of the data for inferring individual traits pertaining to health, wellness, and behavior. To this end, we introduce and evaluate deep neural network models that achieve high prediction performance without requiring any feature engineering when trained directly on the densely sampled multivariate mHealth time series data.
We believe that the present work substantiates both the feasibility and the utility of creating a very large mHealth research cohort, as envisioned by the many large cohort studies currently underway across therapeutic areas and conditions.