Worldwide, there are nearly 10 million new cases of dementia annually, of which Alzheimer’s disease (AD) is the most common. New measures are needed to improve the diagnosis of individuals with cognitive impairment due to various etiologies. Here, we report a deep learning framework that accomplishes multiple diagnostic steps in successive fashion to identify persons with normal cognition (NC), mild cognitive impairment (MCI), AD, and non-AD dementias (nADD). We demonstrate a range of models capable of accepting flexible combinations of routinely collected clinical information, including demographics, medical history, neuropsychological testing, neuroimaging, and functional assessments. We then show that these frameworks compare favorably with the diagnostic accuracy of practicing neurologists and neuroradiologists. Lastly, we apply interpretability methods in computer vision to show that disease-specific patterns detected by our models track distinct patterns of degenerative changes throughout the brain and correspond closely with the presence of neuropathological lesions on autopsy. Our work demonstrates methodologies for validating computational predictions with established standards of medical diagnosis.

Background: Physical activity has emerged as a modifiable behavioral factor to improve cognitive function. However, research on adherence to remote monitoring of physical activity in older adults is limited. Objective: This study aimed to assess adherence to remote monitoring of physical activity in older adults within a pilot cohort from objective user data, providing insights for the scalability of such monitoring approaches in larger, more comprehensive future studies. Methods: This study included 22 participants from the Boston University Alzheimer’s Disease Research Center Clinical Core. These participants opted into wearing the Verisense watch as part of their everyday routine during 14-day intervals every 3 months. Eighteen continuous physical activity measures were assessed. Adherence was quantified daily and cumulatively across the follow-up period. The coefficient of variation was used as a key metric to assess data consistency across participants over multiple days. Day-to-day variability was estimated by calculating intraclass correlation coefficients using a 2-way random-effects model for the baseline, second, and third days. Results: Adherence to the study on a daily basis outperformed cumulative adherence levels. The median proportion of adherence days (wearing time surpassed 90% of the day) stood at 92.1%, with an IQR spanning from 86.9% to 98.4%. However, at the cumulative level, 32% (7/22) of participants in this study exhibited lower adherence, with the device worn on fewer than 4 days within the requested initial 14-day period. Five physical activity measures have high variability for some participants. Consistent activity data for 4 physical activity measures might be attainable with just a 3-day period of device use. Conclusions: This study revealed that while older adults generally showed high daily adherence to the wearable device, consistent usage across consecutive days proved difficult. These findings underline the effectiveness of wearables in monitoring physical activity in older populations and emphasize the ongoing necessity to simplify usage protocols and enhance user engagement to guarantee the collection of precise and comprehensive data.

Most research using digital technologies builds on existing methods for staff‐administered evaluation, requiring a large investment of time, effort, and resources. Widespread use of personal mobile devices provides opportunities for continuous health monitoring without active participant engagement. Home‐based sensors show promise in evaluating behavioral features in near real time. Digital technologies across these methodologies can detect precise measures of cognition, mood, sleep, gait, speech, motor activity, behavior patterns, and additional features relevant to health. As a neurodegenerative condition with insidious onset, Alzheimer disease and other dementias (AD/D) represent a key target for advances in monitoring disease symptoms. Studies to date evaluating the predictive power of digital measures use inconsistent approaches to characterize these measures. Comparison between different digital collection methods supports the use of passive collection methods in settings in which active participant engagement approaches are not feasible. Additional studies that analyze how digital measures across multiple data streams can together improve prediction of cognitive impairment and early‐stage AD are needed. Given the long timeline of progression from normal to diagnosis, digital monitoring will more easily make extended longitudinal follow‐up possible. Through the American Heart Association–funded Strategically Focused Research Network, the Boston University investigative team deployed a platform involving a wide range of technologies to address these gaps in research practice. Much more research is needed to thoroughly evaluate limitations of passive monitoring. Multidisciplinary collaborations are needed to establish legal and ethical frameworks for ensuring passive monitoring can be conducted at scale while protecting privacy and security, especially in vulnerable populations.

Background and Objectives Carotid intima-media thickness (c-IMT) is a measurement of atherosclerosis, a progressive disease that develops as early as childhood and has been linked with cognitive impairment and dementia in the elderly. However, the relationship between c-IMT and midlife cognitive function and the race and social disparities in this relationship remain unclear. We examined the association between c-IMT and cognitive function in midlife among Black and White participants from a semirural community-based cohort in Bogalusa, Louisiana. Methods In this cross-sectional analysis of participants from the Bogalusa Heart Study, linear regression models were used to determine the association between c-IMT dichotomized above the 50th percentile (>0.87 mm), an a demographically standardized global cognitive score (GCS), and individual cognitive domain–based z scores. Stratified analyses were performed to evaluate the impact of race and the individual9s education status. Results A total of 1,217 participants (age 48 ± 5.28 years) were included; 66% (804) self-identified as White, and 34% (413) self-identified as Black. Of those, 58% (708) were women, and 42% (509) were men. Having a c-IMT ≥50th percentile was inversely associated with GCS (B ± SE −0.39 ± 0.18, p = 0.03), independently of cardiovascular risk factors (CVRFs) and achieved education. The effect remained significant in Black and White participants after adjustment for CVRFs (Black participants: B ± SE −1.25 ± 0.45, p = 0.005; White participants: B ± SE −0.92 ± 0.35, p = 0.008) but not for education. The interaction between c-IMT ≥50th percentile and education was significant (p = 0.03), and stratified analysis showed an association with GCS among those with lower achieved education (B ± SE −0.81 ± 0.33, p = 0.013) independently of major CVRFs. Discussion Subclinical atherosclerosis, measured as c-IMT, was associated with worse midlife cognitive function, independently of major CVRFs. The association was buffered by education and may be stronger among Black than White participants, likely due to corresponding structural and social determinants. These findings underscore the importance of establishing preventive measures in midlife and suggest subclinical atherosclerosis as a potential target to prevent cognitive decline.

Background: Smartphone‐based cognitive assessments have emerged as promising tools, bridging gaps in accessibility and reducing bias in Alzheimer disease and related dementia research. However, their congruence with traditional neuropsychological tests and usefulness in diverse cohorts remain underexplored. Methods and Results: A total of 406 FHS (Framingham Heart Study) and 59 BHS (Bogalusa Heart Study) participants with traditional neuropsychological tests and digital assessments using the Defense Automated Neurocognitive Assessment (DANA) smartphone protocol were included. Regression models investigated associations between DANA task digital measures and a neuropsychological global cognitive Z score (Global Cognitive Score [GCS]), and neuropsychological domain‐specific Z scores. FHS participants’ mean age was 57 (SD, 9.75) years, and 44% (179) were men. BHS participants' mean age was 49 (4.4) years, and 28% (16) were men. Participants in both cohorts with the lowest neuropsychological performance (lowest quartile, GCS1) demonstrated lower DANA digital scores. In the FHS, GCS1 participants had slower average response times and decreased cognitive efficiency scores in all DANA tasks ( P <0.05). In BHS, participants in GCS1 had slower average response times and decreased cognitive efficiency scores for DANA Code Substitution and Go/No‐Go tasks, although this was not statistically significant. In both cohorts, GCS was significantly associated with DANA tasks, such that higher GCS correlated with faster average response times ( P <0.05) and increased cognitive efficiency (all P <0.05) in the DANA Code Substitution task. Conclusions: Our findings demonstrate that smartphone‐based cognitive assessments exhibit concurrent validity with a composite measure of traditional neuropsychological tests. This supports the potential of using smartphone‐based assessments in cognitive screening across diverse populations and the scalability of digital assessments to community‐dwelling individuals.

Background: Smartphone‐based digital technology is increasingly being recognized as a cost‐effective, scalable, and noninvasive method of collecting longitudinal cognitive and behavioral data. Accordingly, a state‐of‐the‐art 3‐year longitudinal project focused on collecting multimodal digital data for early detection of cognitive impairment was developed. Methods and Results: A smartphone application collected 2 modalities of cognitive data, digital voice and screen‐based behaviors, from the FHS (Framingham Heart Study) multigenerational Generation 2 (Gen 2) and Generation 3 (Gen 3) cohorts. To understand the feasibility of conducting a smartphone‐based study, participants completed a series of questions about their smartphone and app use, as well as sensory and environmental factors that they encountered while completing the tasks on the app. Baseline data collected to date were from 537 participants (mean age=66.6 years, SD=7.0; 58.47% female). Across the younger participants from the Gen 3 cohort (n=455; mean age=60.8 years, SD=8.2; 59.12% female) and older participants from the Gen 2 cohort (n=82; mean age=74.2 years, SD=5.8; 54.88% female), an average of 76% participants agreed or strongly agreed that they felt confident about using the app, 77% on average agreed or strongly agreed that they were able to use the app on their own, and 81% on average rated the app as easy to use. Conclusions: Based on participant ratings, the study findings are promising. At baseline, the majority of participants are able to complete the app‐related tasks, follow the instructions, and encounter minimal barriers to completing the tasks independently. These data provide evidence that designing and collecting smartphone application data in an unsupervised, remote, and naturalistic setting in a large, community‐based population is feasible.