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Background: Ecological momentary assessments (EMA) are increasingly used to monitor self‐perceived memory and cognitive difficulties. We investigate how traditional self‐reported, recall based assessments of cognitive difficulties correlate with EMA measures. We identify factors explaining shared variance between measures from the 40‐item version of the Cognitive Change Index (CCI) and from EMA daily diaries, and factors explaining unique variance in each assessment. We compare EMA and CCI individually, as well as factors derived from both assessments, in predicting incident mild cognitive impairment (MCI). Methods: In neuropsychologically unimpaired Einstein Aging Study participants, we collected daily digital diary data on 18 cognitive lapses and their perceived disruption of everyday function over 14 days. Additionally, we assessed the conventional recall‐based indicators using the paper‐and‐pencil CCI, referencing both current ability and change over five years. Bayesian Canonical Correlation Analysis (BCCA) was employed to identify factors explaining the relationship between EMA and CCI, and factors explaining the unique variance in each. Separate Cox regressions were employed to assess whether baseline standardized CCI total scores, total number of cognitive lapses reported on EMA, and factors derived from the BCCA, were associated with the incidence of MCI after adjusting for age, gender, race/ethnicity, and depression status. Results: Analyses included 205 community‐dwelling participants (mean age = 77.11, SD = 4.78, 66.82% female, 53.17% white) without MCI at baseline. We identified one shared factor between CCI‐40 and EMA that explains 69% of CCI‐40 variability and 12% of EMA variability (canonical correlation = 0.58). We also found two factors unique to EMA explaining 32% and 27% of the variance. The shared CCI‐EMA factor (HR = 1.56, 95% CI = 1.11‐2.19, p = 0.0017), and total CCI‐40 scores (HR = 1.60, 95% CI = 1.14‐2.25, p = 0.011), significantly predicts MCI incidence at any follow‐up. Neither of total number of cognitive lapses reported on EMA nor BCCA factors unique to EMA significantly predict incidence MCI. Conclusions: Results suggest that traditional assessments and EMA measurements of memory and cognitive difficulties provide both shared and unique information. This information could be harnessed using advanced data analysis techniques to improve the prediction of incident MCI. [ABSTRACT FROM AUTHOR]

Background: The Einstein Aging Study uses smartphones in an intensive burst design to digitally assess cognition up to 6 times daily for 2 weeks once a year. These burst assessments facilitate the estimation of novel cognitive markers including learning effects and asymptotic performance within bursts and over annual assessments. Here, we examine the influence of two blood‐based biomarkers, pTau‐181 and Neurofilament Light protein (NfL), on cognitive performance measured by digital cognitive markers at baseline and across 6 annual burst assessments. Method: Participants were 293 older adults (Age = 77.4±4.9) free of dementia at baseline. Negative exponential models were used to examine two primary features of response time from the Symbol Search Test of processing speed: (1) learning rate across days within a burst and its change in rate of learning over annual follow‐up; and (2) asymptotic performance (i.e., fastest response time) at baseline and asymptotic change (the rate in which asymptotic performance changes across years). Asymptotic performance is thought to reflect best possible performance and to be relatively free of practice effects. The associations of pTau‐181 and NfL with these features at baseline and change over annual follow‐up were evaluated, adjusting for baseline age, sex, years of education and race/ethnicity. Result: Primary model results are summarized in Table 1. The expected trajectory of symbol search response time across 16 study days within each of the six annual waves, for baseline levels of pTau‐181 and NfL is shown in Figures 1 and 2. Higher levels of both Tau‐181 and of NfL were associated with slower rates of learning in the baseline burst. Higher levels of these BBB at baseline were also associated with slower learning rates from burst to burst (over years) and with greater slowing in asymptotic performance over annual follow‐ups. Conclusion: Higher levels of BBB of Tau and of neurodegeneration are associated with slower learning and with slower response times. These novel parameters derived from ambulatory cognitive testing show expected relationships with biomarkers supporting their validity. Future applications of ambulatory cognition could include the identification of subtle cognitive impairment and in measuring within person change in cognition in clinical trials or clinical practice. [ABSTRACT FROM AUTHOR]

Background: Conventional cognitive assessment typically provides a single measure of performance at a single point in time in a laboratory setting. Smartphone‐based ecological momentary assessments (EMA) facilitate short‐term intensive assessment of cognitive performance in people's natural environments. Though heterogeneous variance in digital cognitive assessments has been observed between older adults with mild cognitive impairment (MCI) and cognitively unimpaired (CU), the independent contribution of intra‐individual variability in performance to detection of MCI is poorly characterized in diverse populations of older adults. We examine the ability of digital cognitive assessments of processing speed in discriminating MCI from CU in older adults from a racially/ethnically diverse community sample. Method: Einstein Aging Study (EAS), smartphone‐based digital assessments of cognition, included the response time‐based symbol match test of processing speed. This was assessed 6 times per day over two weeks and was repeated annually. MCI was defined based on in‐house neuropsychological evaluations using Jak‐Bondi criteria. Statistical methods for modeling MCI using digital cognitive assessments include pattern‐mixture discriminant analysis based on heterogeneous linear mixed effects model for digital cognitive measures given MCI, joint modeling using shared random effects with random location scale for digital cognitive measures, and quadratic logistic regression models. All methods take into account the unbalanced adherence rate among individuals and control for covariates including age, sex, years of education, and race/ethnicity. Stratified analyses by race/ethnicity among non‐Hispanic (NH) Whites and Blacks were performed. Result: Among 311 systematically recruited community‐dwelling non‐demented older adults from the EAS (Mean age = 77.5 years, SD = 4.8; 67.5% Female; 45.3% non‐Hispanic (NH) White, 40.8% NH‐Black), 97 (31.2%) individuals had MCI. Adherence to the EMA protocol was slightly better in individuals who were CU compared to those with MCI. Results from the quadratic logistic model (the easiest to apply) showed evidence of contribution from the quadratic terms that reflect heterogeneous intra‐individual variability in all samples and particularly among NH‐Blacks (p = 0.008). Conclusion: We observe evidence that intra‐individual variability in processing speed improves the ability to discriminate cognitive impairment, particularly among NH‐Black older adults. Leveraging digital technology and performance variability across frequent assessments in the smartphone‐based EMA offers novel opportunities to identify cognitive impairment. [ABSTRACT FROM AUTHOR]

Background: Although cognitive lapses are a daily occurrence for many older adults, widely used self‐reports of cognitive function ask respondents to recollect lapses occurring over months or years. We used daily diaries to capture memory and other cognitive difficulties close in time to their occurrence and determined whether daily reports predicted concurrent and/or new cases of mild cognitive impairment (MCI) in the next year (i.e., at annual follow‐up). Method: In the Einstein Aging Study, daily digital diary indicators of cognitive lapses were assessed each evening over a two‐week period (82% of diaries completed). Conventional recall‐based indicators were the Cognitive Change Index total score and a single item capturing worsening memory (with/without worry), both assessed at each annual visit. Using time‐dependent lagged models with generalized estimating equations (GEE) to correct for within person correlation, we evaluated the association of current cognitive lapses with the probability of being MCI at the current or next annual visit among those diagnosed with MCI after baseline (n = 31 new cases). Result: In 307 community‐dwelling participants (Mage = 77.05, SD = 4.95; 67% female, 47% White) who were free of MCI at baseline, higher numbers of daily problems significantly predicted the likelihood of being diagnosed with MCI at the next annual visit (average number of visits = 2.72, range 1‐6 years). For every additional daily cognitive lapse, there was a 5% greater odds of being classified at the next visit (OR = 1.05, 95% CI: 1.01‐1.09). By contrast, conventional indicators did not significantly predict transition to MCI at the next visit (ps =.48 and.62, respectively). However, the single item regarding worsening memory was related to concurrent MCI diagnostic status (p<.01). Sensitivity analyses examining the first 3 days or first 7 days to determine the number of daily reports required to identify new MCI cases indicated that both 3 and 7 days significantly predicted future MCI (OR = 1.34, 95% CI: 1.12‐1.61; OR = 1.07, 95% CI: 1.00‐1.14, respectively). Conclusion: A brief daily diary assessment of cognitive lapses predicted new cases of MCI. The combination of daily and conventional methods may represent complementary approaches that offer the optimal approach for capturing self‐perceived cognitive deficits associated with current and future MCI. [ABSTRACT FROM AUTHOR]

Background: Digital cognitive assessments repeated over multiple days improve reliability compared to "single‐shot" assessments due to within person variability in function. Further, digital assessments are possible when clinic visits are not. We show the utility of digital approaches using data from the Einstein Aging Study (EAS) diverse cohort of community‐dwelling older adults. Method: Participants were administered conventional and ambulatory digital assessments annually. The later consisted of a 2‐week burst of digital cognitive and behavioral/exposure measures obtained 6 times/day. Digital cognitive assessments included three tests (processing speed (Symbol Match), visual short‐term memory binding (Color Shape), and spatial working memory (Grid Memory)). Exposure assessment included daily stress, pain, and subjective sleep quality. Feasibility was measured as compliance defined as proportion of completed assessments. Frequency of annual assessments completed was evaluated throughout the COVID‐19 pandemic. For digital measures, reliability was calculated using intraclass correlations (ICCs) across days within a burst and across annual visits. ICCs for conventional measures across annual visits were also computed. To assess predictive validity, we used Cox proportional hazard models to examine whether mean and variability of performance on the 3 digital cognitive tasks predicted incident MCI. Result: Compliance rates overall were 88% and were ≥ 85% in strata based on MCI status, age, race/ethnicity, education or prior experience with smartphones (Table 1). Data collection was maintained throughout the pandemic (Figure 1). ICCs for the digital cognitive measures was consistently high (>0.93). The same was true for exposure measures of stress, subjective sleep and pain (>.88). ICCs across annual follow‐up for digital measures ranged from 0.87 to 0.96 compared to 0.51 to 0.87 for conventional cognitive measures. Regarding predictive validity, worse mean performance in all three digital cognitive measures and higher within‐person variability (SD) in symbol search and color shape were associated with higher risk of incident MCI. Conclusion: Digital assessments are feasible to administer, have high reliability and show predictive validity for MCI. They may provide useful tools in observational studies and clinical trials due to improved classification of cognitive status and feasibility in situations where in person assessments are not possible. [ABSTRACT FROM AUTHOR]