Mobile technologies are increasingly used to measure cognitive function outside of traditional clinic and laboratory settings. Although ambulatory assessments of cognitive function conducted in people’s natural environments offer potential advantages over traditional assessment approaches, the psychometrics of cognitive assessment procedures have been understudied. We evaluated the reliability and construct validity of ambulatory assessments of working memory and perceptual speed administered via smartphones as part of an ecological momentary assessment (EMA) protocol in a diverse adult sample (N=219). Results indicated excellent between-person reliability (≥.97) for average scores, and evidence of reliable within-person variability across measurement occasions (.41–.53). The ambulatory tasks also exhibited construct validity, as evidence by their loadings on working memory and perceptual speed factors defined by the in-lab assessments. Our findings demonstrate that averaging across brief cognitive assessments made in uncontrolled naturalistic settings provide measurements that are comparable in reliability to assessments made in controlled laboratory environments.

Background and Objective: Within-person variability in cognitive performance has emerged as a promising indicator of cognitive health with potential to distinguish normative and pathological cognitive aging. We use a smartphone-based digital health approach with ecological momentary assessments (EMA) to examine differences in variability in performance among older adults with mild cognitive impairment (MCI) and those who were cognitively unimpaired (CU).Method: A sample of 311 systematically recruited, community-dwelling older adults from the Einstein Aging Study (Mean age = 77.46 years, SD = 4.86, Range = 70–90; 67% Female; 45% Non-Hispanic White, 40% Non-Hispanic Black) completed neuropsychological testing, neurological assessments, and self-reported questionnaires. One hundred individuals met Jak/Bondi criteria for MCI. All participants performed mobile cognitive tests of processing speed, visual short-term memory binding, and spatial working memory on a smartphone device up to six times daily for 16 days, yielding up to 96 assessments per person. We employed heterogeneous variance multilevel models using log-linear prediction of residual variance to simultaneously assess cognitive status differences in mean performance, within-day variability, and day-to-day variability. We further tested whether these differences were robust to the influence of environmental contexts under which assessments were performed.Results: Individuals with MCI exhibited greater within-day variability than those who were CU on ambulatory assessments that measure processing speed (p < 0.001) and visual short-term memory binding (p < 0.001) performance but not spatial working memory. Cognitive status differences in day-to-day variability were present only for the measure of processing speed. Associations between cognitive status and within-day variability in performance were robust to adjustment for sociodemographic and contextual variables.Conclusion: Our smartphone-based digital health approach facilitates the ambulatory assessment of cognitive performance in older adults and the capacity to differentiate individuals with MCI from those who were CU. Results suggest variability in mobile cognitive performance is sensitive to MCI and exhibits dissociative patterns by timescale and cognitive domain. Variability in processing speed and visual short-term memory binding performance may provide specific detection of MCI. The 16-day smartphone-based EMA measurement burst offers novel opportunity to leverage digital technology to measure performance variability across frequent assessments for studying cognitive health and identifying early clinical manifestations of cognitive impairment.

These findings suggest that anticipatory processes can produce harmful effects on cognitive functioning that are independent of everyday stress experiences. This may identify an important avenue to mitigate everyday cognitive lapses among older adults.

Introduction The projected growth of Alzheimer's disease (AD) and AD‐related dementia (ADRD) cases by midcentury has expanded the research field and impelled new lines of inquiry into structural and social determinants of health (S/SDOH) as fundamental drivers of disparities in AD/ADRD. Methods In this review, we employ Bronfenbrenner's ecological systems theory as a framework to posit how S/SDOH impact AD/ADRD risk and outcomes. Results Bronfenbrenner defined the “macrosystem” as the realm of power (structural) systems that drive S/SDOH and that are the root cause of health disparities. These root causes have been discussed little to date in relation to AD/ADRD, and thus, macrosystem influences, such as racism, classism, sexism, and homophobia, are the emphasis in this paper. Discussion Under Bronfenbrenner's macrosystem framework, we highlight key quantitative and qualitative studies linking S/SDOH with AD/ADRD, identify scientific gaps in the literature, and propose guidance for future research. Highlights Ecological systems theory links structural/social determinants to AD/ADRD. Structural/social determinants accrue and interact over the life course to impact AD/ADRD. Macrosystem is made up of societal norms, beliefs, values, and practices (e.g., laws). Most macro‐level determinants have been understudied in the AD/ADRD literature.

Background: Amyloid-β positivity (Aβ+) based on PET imaging is part of the enrollment criteria for many of the clinical trials of Alzheimer's disease (AD), particularly in trials for amyloid-targeted therapy. Predicting Aβ positivity prior to PET imaging can decrease unnecessary patient burden and costs of running these trials. Objective: The aim of this study was to evaluate the performance of a machine learning model in estimating the individual risk of Aβ+ based on gold-standard of PET imaging. Methods: We used data from an amnestic mild cognitive impairment (aMCI) subset of the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to develop and validate the models. The predictors of Aβ status included demographic and ApoE4 status in all models plus a combination of neuropsychological tests (NP), MRI volumetrics, and cerebrospinal fluid (CSF) biomarkers. Results: The models that included NP and MRI measures separately showed an area under the receiver operating characteristics (AUC) of 0.74 and 0.72, respectively. However, using NP and