The ubiquity and remarkable technological progress of wearable consumer devices and mobile-computing platforms (smart phone, smart watch, tablet), along with the multitude of sensor modalities available, have enabled continuous monitoring of patients and their daily activities. Such rich, longitudinal information can be mined for physiological and behavioral signatures of cognitive impairment and provide new avenues for detecting MCI in a timely and cost-effective manner. In this work, we present a platform for remote and unobtrusive monitoring of symptoms related to cognitive impairment using several consumer-grade smart devices. We demonstrate how the platform has been used to collect a total of 16TB of data during the Lilly Exploratory Digital Assessment Study, a 12-week feasibility study which monitored 31 people with cognitive impairment and 82 without cognitive impairment in free living conditions. We describe how careful data unification, time-alignment, and imputation techniques can handle missing data rates inherent in real-world settings and ultimately show utility of these disparate data in differentiating symptomatics from healthy controls based on features computed purely from device data.

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. 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.