Alzheimer’s disease and related dementias (ADRD) is a growing epidemic, and in the absence of effective treatment, disease burden increases as the population ages. In both ADRD and mild cognitive impairment (MCI), there is significant temporal variability in disease progression, increasing the difficulty for managing patient comfort and safety. Early detection of symptomatic states and continuous monitoring are regarded as effective measures to minimize the impact of the disease as various forms of intervention can provide opportunities for treatment, compensation and coping. However, current clinic-based cognitive and behavioral assessments have numerous shortcomings; they are largely non-quantitative and clinicians often have difficulty determining if there has been significant changes in neurologic condition between visits. Additionally, assessments are obtained infrequently, and do not objectively account for disease-related behaviors that could be revealed in daily activities. In this project, we propose to advance new computational approaches and analytics to identify digital biomarkers for ADRD detection, prediction and monitoring outside the clinic. This technology-driven approach is based on sensor data passively acquired from commodity smartphones and wearables, and provides the foundation for a novel embedded assessment of cognitive status through continuous monitoring.
This proposal presents several research opportunities. Firstly, we will advance passive and continuous data collection methods using multimodal sensing. Challenges we will address include optimizing battery use for long-term data capture, and mitigating privacy concerns by performing on-device data and feature pre-processing. Secondly, we will be building on state-of-the-art research techniques in behavior and context recognition, speech analysis, and machine learning to identify digital biomarkers of Alzheimer’s disease and related disorders. We will leverage these biomarkers to build computational models for disease stage characterization and prediction, and individualize them by incorporating race and ethnicity risk factors as priors. Lastly, to facilitate the use of these models and digital biomarkers in clinical practice, we will advance a novel visual analytics interface towards helping physicians and health practitioners interact with the acquired sensor data, validate the digital biomarkers, verify model results, and forecast the progression of disease.
A clear and specific clinical need motivates this proposal: improved and continuous understanding, monitoring, characterization, assessment and prediction of a prevalent neuro-cognitive condition in naturalistic settings. ADRDs are difficult and costly diseases to treat, affecting millions of people in the U.S alone. Our approach provides the foundation for a new direction in the early detection and prediction of this devastating and highly-debilitating condition.
Jared Benge from The TechANS lab participate in this 4-year R01 grant award from the National Institute of Health (NIH) project of Edison Thomaz of Texas ECE and fellow researchers from The University of Texas at Austin.