Identifying older adults at risk for Alzheimer’s Disease based on smartphone data obtained during wayfinding in the real world

Abstract

AbstractAlzheimer’s disease (AD), as a leading cause for disability and death in old age, represents a major burden to healthcare systems worldwide. For the development of disease-modifying interventions and treatments, the detection of cognitive changes at the earliest disease stages is crucial. Recent advancements in mobile consumer technologies provide new opportunities to collect multi-dimensional data in real-life settings to identify and monitor at-risk individuals. Based on evidence showing that deficits in spatial navigation might constitute one of the earliest AD symptoms, we assessed whether older adults who report subjective cognitive decline (SCD) but score normally on neuropsychological assessments show differences in smartphone-assisted wayfinding behavior compared with cognitively healthy older and younger adults. Guided by a mobile application, participants had to find locations along a short route on the medical campus of the Magdeburg university. We show that performance measures that were extracted from GPS and user input data distinguish between the groups. In particular, the number of orientation stops was predictive of the SCD status in older participants. Our data suggest that cognitive changes, associated with an elevated AD risk, can be inferred from smartphone data, collected during a brief episode of an everyday behavior.