We have limited understanding of how older adults use smartphones, how their usage differs from younger users, and the causes for those differences. As a result, researchers and developers may miss promising opportunities to support older adults or offer solutions to unimportant problems. To characterize smartphone usage among older adults, we collected iPhone usage data from 84 healthy older adults over three months. We find that older adults use fewer apps, take longer to complete tasks, and send fewer messages. We use cognitive test results from these same older adults to then show that up to 79% of these differences can be explained by cognitive decline, and that we can predict cognitive test performance from smartphone usage with 83% ROCAUC. While older adults differ from younger adults in app usage behavior, the "cognitively young" older adults use smartphones much like their younger counterparts. Our study suggests that to better support all older adults, researchers and developers should consider the full spectrum of cognitive function.

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.