A combination of technology and deep data mining has been used to differentiate individuals with mild cognitive impairment (MCI) or Alzheimer’s disease (AD) from individuals without cognitive impairments. In the 12-week Lilly Exploratory Digital Assessment Study (NCT01459016), 31 participants with MCI or AD, age 60 to 75, had
when compared with 84 age- and sex-matched participants without cognitive impairment. Differences were also seen between participants with MCI compared with those with AD, but the number of participants in this feasibility study was too small to determine the significance of these differences. Future studies to address the possibility of using these methods to differentiate MCI from AD are planned.
"While further research is needed, the study findings provide important insight into the potential benefits of wearable devices in identifying chronic health conditions such as MCI, Alzheimer's disease, and dementia,” said Divakar Ramakrishnan, PhD, chief digital officer, Eli Lilly and Company. “These findings could inform subsequent research that may eventually lead to early screening or detection tools for neurodegenerative conditions."
Data was collected passively from iPhones and iWatches and included app usage, lock/unlock times, and message frequency and other measures. Participants also performed active tasks including rating mood and energy, shape-dragging, tapping tasks, reading, and picture description.
The data was presented in a paper at the at the Association for Computing Machinery's KDD conference in Anchorage, AK on August 8, 2019. Researchers from Evidation Health (San Mateo, CA), Apple (Cupertino, CA), and Eli Lilly (Indianapolis, IN) collaborated on this study. The trial collected and analyzed 16 terabytes of data using Evidation's data analysis platform.