Honghuang Lin, UMass Chan Medical School.

MassAITC Cohort: Year 2 (AD/ADRD)

Despite enormous efforts, therapeutic clinical trials for Alzheimer’s disease (AD) have largely failed, possibly because interventions are initiated too late into the disease course when neurodegeneration has begun and is irreversible. Early detection of people at high risk of AD is thus particularly important to help in the effort to develop prevention and treatment options. We hypothesize that subtle changes in motor function may serve as a potential biomarker of early signs of cognitive impairment. Tracking changes in cadence will enable the assessment of potential parallel trajectories of motor/cognitive decline and may prove to be an indicator of declining cognition.

This study will leverage a large collection of physical activity and cognitive assessment data, and derive novel digital phenotypes from commercially available wearable devices. We will then assess the correlation of these novel measures with declining cognitive function, and develop advanced machine learning models to identify people at high risk to develop cognitive impairment. Given the increasing popularity of wearable devices, the knowledge and tools developed from the current project could be readily applicable to the general population for the large-scale screening of cognitive health.