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.

Without effective prevention and treatment strategies, the expected number of older adults in the United States with dementia will be more than 13 million by 2050.¹ As there is great need to identify modifiable risk factors for dementia, hearing and visual impairments may be potentially modifiable risk factors, as they have been shown to be individually associated with increased risk for dementia.^2-5 In older adults, the risk of cognitive decline or impairment^6-11 and of dementia^12-14 are also associated with combined hearing and visual impairments, which often occur together as dual sensory impairment (DSI).^15,16 The prevalence of DSI in the United States has been found to be as high as 15% in adults, with the highest prevalence among adults 80 years and older.^16,17 In prior work, we found an increased risk for all-cause dementia and Alzheimer disease (AD) associated with DSI among older adults.¹⁸ Deficits in hearing and vision may lead to cognitive impairment and dementia by increasing social isolation, depression, and reducing physical activity.^19-21 Additionally, sensory and cognitive impairment may share similar, underlying pathophysiology, such as cerebrovascular disease or neurodegeneration.^22-27 Those with DSI may be especially at risk of cognitive dysfunction as compensatory mechanisms are more limited (ie, sensory substitution) to preserve functioning.

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.

Importance: Being physically active is protective against dementia. Yet, it is unknown when during the adult life course physical activity is most associated with dementia risk. Objective: To determine whether higher physical activity levels in early adult life, midlife, or late life are associated with lower risk of all-cause or Alzheimer disease (AD) dementia. Design, Setting, and Participants: This prospective cohort study used data from the Framingham Heart Study Offspring cohort. The offspring of participants in the original Framingham Heart Study cohort who were dementia free and had physical activity measured at baseline (early adult life [1979-1983], midlife [1987-1991], or late life [1998-2001]) were followed up for a mean (SD) of 37.2 (7.1), 25.9 (8.5), or 14.5 (6.6) years for the development of incident all-cause or AD dementia until December 31, 2023. Exposure: Physical activity was self-reported using the physical activity index, a composite score weighted by hours spent sleeping and in sedentary, slight, moderate, or heavy activities. Physical activity was divided into quintiles (Q). Main Outcome and Measure: All-cause and AD dementia were classified by expert consensus based on established diagnostic criteria. Results: This study included 1526 early adult–life (mean [SD] age, 36.7 [4.7] years; 821 [53.8%] female), 1943 midlife (mean [SD] age, 54.0 [5.8] years; 1010 [52.0%] female), and 885 late-life (mean [SD] age, 71.0 [4.5] years; 473 [53.4%] female) participants. There were 567 cases of incident all-cause dementia during follow-up. Higher levels of midlife and late-life physical activity were associated with lower risk of all-cause dementia. Midlife and late-life physical activity levels in Q4 or Q5 were associated with lower risk of all-cause dementia compared with Q1 (midlife Q4: HR, 0.60; 95% CI, 0.41-0.89; midlife Q5: HR, 0.59; 95% CI, 0.40-0.88; late-life Q4: HR, 0.64; 95% CI, 0.42-1.00; late-life Q5: HR, 0.55; 95% CI, 0.35-0.87). There were no associations between early adult–life physical activity and dementia risk. Findings were similar for incident AD (369 cases). Conclusions and Relevance: In this cohort study of adults in the Framingham Heart Study Offspring cohort, higher levels of midlife and late-life physical activity were associated with similar reductions in risk of all-cause and AD dementia. These findings may inform future efforts to delay or prevent dementia through timing interventions during the most relevant stages of the adult life course.