Dementia with Lewy bodies (DLB) is difficult to diagnose early in its disease course due to the overlap in initial symptoms with Alzheimer's disease (AD). Many individuals with DLB therefore experience long delays in receiving an accurate diagnosis. This lack of sensitivity in the consensus diagnosis for DLB, particularly outside of specialty-care centers, means that DLB is associated with delayed interventions and increased caregiver burden. We thus propose a two-phase study that investigates the utility of combining data from wearable sensors, ecological momentary assessments (EMAs), and traditional measures as a multidomain approach for the early diagnosis of DLB. To collect and analyze these integrated objective measurements, we will establish a research infrastructure that includes an interdisciplinary team of engineers, clinicians, researchers, and biotechnology companies. In the R21, we will estimate and compare the distributions of cognitive, motor, sleep, and behavioral monitoring profiles in subjects with probable DLB (n=20) and AD dementia (n=30). If the R21 demonstrates the feasibility of using wearable sensors and EMAs in this population and their ability to improve discrimination between DLB and AD, we will proceed to the next study phase. The R33 aims to characterize and compare the trajectories of these same traditional and novel cognitive, motor, sleep, and behavioral monitoring profiles in subjects with mild cognitive impairment (MCI) and one or more core DLB features (MCI-DLB; n=75) and in subjects with amnestic MCI and no core DLB features (MCI-AD; n=25). We hypothesize that a composite measure combining information from the baseline and trajectory measures in the longitudinal R33 will improve discrimination between individuals with MCI-DLB who will convert to DLB, AD, or remain MCI. We anticipate that the results of this study will have tangible benefits to researchers, clinicians, patients, and the caretakers of patients. The improved ability to differentiate early DLB from early AD will assist researchers in selecting appropriate subjects for clinical trials of AD and related disorders (ADRD; e.g., DLB). Moreover, because of the longitudinal nature of the R33, researchers and clinicians will have accessible data on disease progression, which can be tremendously helpful in evaluating the efficacy of treatment. Most importantly, by improving the diagnosis of early DLB, clinicians will be better equipped to avoid prescribing potentially harmful treatments (e.g., antipsychotics for DLB) and to more accurately tailor current or future interventions to patients earlier in their disease course at the time that such interventions are most likely to be effective.
Dementia with Lewy bodies (DLB) is an aging disorder that can harm thinking, memory, movements, sleep, and behavior. Because it is difficult to diagnose DLB, particularly early in its disease course, many individuals with DLB experience long delays in receiving a diagnosis, which then delays interventions and causes significant caregiver burden. To address these delays, this study will investigate wearable technologies and standard clinical observations that together have the potential to improve the diagnosis of early DLB.
