Dementia and other age-related neurodegenerative diseases such as Alzheimer's Disease (AD) and Alzheimer'sDisease Related Dementias (ADRD) cause a marked decrease in quality of life for patients, an increased burdenof care, and, for adults over 65, are a leading cause of death. Therefore, better understanding of the nature ofdementia is vital to the development and assessment of effective treatment options. Despite the grim lookout,clinical research suggests that intervening the disease before irreversible brain damage occurs is a key step toimprove the outcomes of AD/ADRD. Specifically, Mild Cognitive Impairment (MCI), the stage before the onset ofdementia, could provide the largest "window of opportunity" for lifestyle interventions that can delay/prevent MCIand dementia. Our team has reported lifestyle changes which brings a window of opportunity for the diseasetreatment prior to irreversible brain damages. Several studies have demonstrated a relationship between MCI and driving performance including drivingbehaviors. Although driving features provide an unobstructive window of opportunity to test the brainresponses under everyday cognitive challenges, it is currently unrealistically costly and labor-intensive to bewidely deployed. We hypothesize an unobstructive, continuous, economical system capable of assessingdriving signatures that can detect early stages of neurodegenerative diseases would offer a window to reverseAD and ADRD. In this work, we propose to develop and validate an unobtrusive and car-make agnosticsensing system, named Smart Pad, for daily assessment of driving indicators under free-living conditions. Thesystem: 1) automatically records and analyzes data with the integrated biosensor array and a mobile App toevaluate driving; 2) uses the biosensors array to detect not only driving habits, but also new drivingperformance indicators and unprecedented driver's biomarker (posture and metabolic rate), which arecorrelated with detrimental cognitive stages, and 3) is empowered by an Artificial Intelligence (AI) algorithm topredict cognitive performance and MCI. In this Phase I proposal, we will leverage the Smart Pad prototypedesign that we have developed and optimize it to for the diagnosis of driver's cognitive status.The TF Health Co.-ASU-BNI team will work together to build the unobtrusive, vehicle-based sensingsystem for daily assessment of driver performance and biometrics, and age-related cognitive decline todevelop new technology for early diagnosis of dementia in free-living condition, seeking to improve patient careoptions and promote cognitive decline prevention interventions.
Public Health Relevance Statement: The proposed project targets the detection of early neurodegenerative diseases, before the onset of
dementia, to delay/prevent the growing and debilitating Alzheimer Disease and Alzheimer's Related
Dementias (AD/ADRD) through affordable and practical lifestyle interventions. Our prior work and
literature strongly support our approach, and if successful, not only will we learn more about early
detection and prevention in free-living condition, we can bring an affordable, easy-to-use, tool to
healthcare professionals and the general public to fight AD/ADRD.
Project Terms: <21+ years old>
