Online platforms are an efficient means to detect early cognitive decline, but few studies have investigated the relationship between remotely collected subjective cognitive change and cognitive decline. We hypothesized that the Everyday Cognition Scale (ECog), a subjective change measure, predicts longitudinal change in cognition in Brain Health Registry (BHR), an online registry for neuroscience research. From the BHR database, we included participants aged 55+ who completed both the baseline ECog and repeated administrations of the CANTAB® Paired Associates Learning (PAL) test. Both self-reported ECog (Self-ECog) and study partner ECog (SP-ECog), and two PAL scores (first attempt memory score [FAMS] and total errors adjusted [TEA]) were assessed. We used multiple ECog scoring outputs, based on previously established cut-offs for likely impairment (Self-ECog positive [total score ≥1.31], SP-ECog positive [total score ≥1.36], and ECog consistent [any item≥3]). A linear mixed effects model was employed to assess the effect of baseline ECog on longitudinal change in PAL. Additionally, logistic regression models were used to assess the ability of ECog to identify ‘decliners’, who exhibited the worst PAL progression slopes corresponding to the fifth percentile and below. The study included a total of 16,683 participants, who were followed for 11.49±11.53 months. Both Self-ECog positive (estimate = -0.01, p <0.0019) and Self-ECog consistent (estimate = -0.008, p <0.0085) were significant predictors for longitudinal change in PAL FAMS after adjusting for age, gender, education, race, depression, family history of Alzheimer’s disease (AD), taking AD medication, and self-reported impairment. Those who were SP-ECog positive (Odds ratio [95% confidence interval] = 1.760 [1.143–2.667]) and SP-ECog consistent (2.021 [1.344–3.021]) had higher probability of being decliners based on PAL FAMS. Regarding the prediction of PAL TEA decliner, both Self-ECog consistent (1.248 [1.043–1.494]) and Self-ECog total (1.287 [1.054–1.557]) were associated with higher odds of being decliners. In the BHR’s unsupervised online setting, ECog demonstrated utility in predicting longitudinal progression in PAL scores, both in terms of continuous changes and when dichotomized as a decliner. Online, self-administered measures of subjective cognitive change, together with objective neuropsychological test results have great potential to identify individuals with cognitive impairments.

Abstract Backgrounds Digital, online assessments are efficient means to detect early cognitive decline, but few studies have investigated the relationship between remotely collected subjective cognitive change and cognitive decline. We hypothesized that the Everyday Cognition Scale (ECog), a subjective change measure, predicts longitudinal change in cognition in the Brain Health Registry (BHR), an online registry for neuroscience research. Methods This study included BHR participants aged 55 + who completed both the baseline ECog and repeated administrations of the CANTAB® Paired Associates Learning (PAL) visual learning and memory test. Both self-reported ECog (Self-ECog) and study partner-reported ECog (SP-ECog), and two PAL scores (first attempt memory score [FAMS] and total errors adjusted [TEA]) were assessed. We estimated associations between multiple ECog scoring outputs (ECog positive [same or above cut-off score], ECog consistent [report of consistent decline in any item], and total score) and longitudinal change in PAL. Additionally we assessed the ability of ECog to identify ‘decliners’, who exhibited the worst PAL progression slopes corresponding to the fifth percentile and below. Results Participants (n = 16,683) had an average age of 69.07 ± 7.34, 72.04% were female, and had an average of 16.66 ± 2.26 years of education. They were followed for an average of 2.52 ± 1.63 visits over a period of 11.49 ± 11.53 months. Both Self-ECog positive (estimate = -0.01, p < 0.001, R²m = 0.56) and Self-ECog consistent (estimate=-0.01, p = 0.002, R²m = 0.56) were associated with longitudinal change in PAL FAMS after adjusting demographics and clinical confounders. Those who were Self-ECog total (Odds ratio [95% confidence interval] = 1.390 [1.121–1.708]) and SP-ECog consistent (2.417 [1.591–3.655]) had higher probability of being decliners based on PAL FAMS. Conclusion In the BHR’s unsupervised online setting, baseline subjective change was feasible in predicting longitudinal decline in neuropsychological tests. Online, self-administered measures of subjective cognitive change might have a potential to predict objective subjective change and identify individuals with cognitive impairments.

In Alzheimer’s disease research, subjective report of cognitive and functional decline from participant-study partner (SP) dyads is an efficient method of assessing cognitive impairment and risk of clinical progression. The extent to which discordance (disagreement) between self- and SP–report is associated with diagnosis of cognitive impairment is not known. We tested the hypothesis that discordance between baseline self- and SP-report Everyday Cognition Scale (ECog) scores was associated with greater probability of mild cognitive impairment (MCI) diagnosis. Dyads enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the University of California, San Francisco Brain Health Registry (BHR), an online longitudinal aging-related research registry, completed an online adaptation of the 39-item ECog to assess subjective change across six cognitive domains. We derived four metrics of discordance between participant and SP ECog scores (dyadic discordance): Raw Score Difference, Absolute Score Difference, Overreport Score, and Underreport Score (Table 1). In the ADNI cohort, we fit a logistic regression model for each of the discordance metrics to independently evaluate their association with MCI diagnosis, after adjusting for dyad relationship and sociodemographic factors. Then, to further evaluate the predictive utility of these measures, we carried out a model selection procedure using cross-sectional data collected from ADNI dyads (N = 921; Table 1). Finally, we externally validated the model in a BHR cohort with clinically confirmed diagnoses (N = 279; Table 1). Higher Raw and Absolute Score Difference, greater Underreport scores, and lower Overreport scores were associated with greater probability of MCI in the ADNI cohort (Table 2). The model selection procedure identified a number of highly predictive variables, which were then included in a model that was externally validated in the BHR cohort. This model distinguished diagnostic groups in the BHR cohort with AUC = 0.892, Sensitivity = 0.61, Specificity = 0.95 (Figure 1) based on a restricted cubic spline regression model. Results indicate that ECog score discordance is associated with MCI diagnosis. The selected model showcased good predictive performance in the validation cohort, and highlights the potential utility of subjective dyadic discordance metrics to help identify older adults with MCI in diverse settings.

Scalable, efficient methods are needed to enroll diverse populations of older adults into AD observational studies and clinical trials. We evaluated preliminary feasibility of a novel, digital, culturally informed approach to recruit and screen participants for the Alzheimer’s Disease Neuroimaging Initiative (ADNI4). Digital advertising tailored towards Black/African American and Latinx older adults residing near six clinical ADNI sites directed potential participants to a recruitment website ( Figure 1 ). They completed digital surveys of demographics, ADNI exclusion criteria, memory concerns and changes, self-report cognitive impairment, and the Everyday Cognition Scale (ECog)-12 item. Participants also completed Novoic Storyteller, a self-administered speech-based cognitive test. Digital assessment results were used to prioritize those from minoritized ethnocultural groups, with low educational attainment, and possible Mild Cognitive Impairment (MCI) for remote blood draw at local Quest phlebotomy centers to obtain AD plasma biomarkers. Completion rates and digital assessment performance were compared between minoritized and non-minoritized groups. Of 989 participants recruited from digital advertising efforts who provided contact information, 230 (23%) signed consent, and 183 (19%) completed at least one remote assessment ( Table 1 ). 45% were from minoritized ethnocultural groups, 4% had ≤12 years of education, and 53% had evidence of possible MCI from digital assessments. 34 (19%) were referred to the plasma biomarker study, of which 21% completed blood draw. Compared to non-minoritized participants, minoritized participants were less likely to report a diagnosed cognitive impairment or a family history of AD, less likely to have ECog scores indicative of MCI, and less likely to have a study partner. Minoritized participants had lower ECog and Novoic Storyteller completion rates. Recruitment and assessment of a diverse cohort of older adults, including those with possible cognitive impairment, is feasible using culturally informed digital advertising. Improving study engagement and achieving educational diversity are key challenges. This approach is now being scaled up to facilitate recruitment into in-clinic ADNI4, with the goal of enrolling 500 new participants: >50% from minoritized groups, 40% with MCI, and 80% amyloid positive across diagnostic groups. This approach can be adapted to facilitate recruitment and longitudinal assessment in other AD studies and trials.

The overall goal of the Alzheimer's Disease Neuroimaging Initiative (ADNI) is to optimize and validate biomarkers for clinical trials while sharing all data and biofluid samples with the global scientific community. ADNI has been instrumental in standardizing and validating amyloid beta (Aβ) and tau positron emission tomography (PET) imaging. ADNI data were used for the US Food and Drug Administration (FDA) approval of the Fujirebio and Roche Elecsys cerebrospinal fluid diagnostic tests. Additionally, ADNI provided data for the trials of the FDA-approved treatments aducanumab, lecanemab, and donanemab. More than 6000 scientific papers have been published using ADNI data, reflecting ADNI's promotion of open science and data sharing. Despite its enormous success, ADNI has some limitations, particularly in generalizing its data and findings to the entire US/Canadian population. This introduction provides a historical overview of ADNI and highlights its significant accomplishments and future vision to pioneer "the clinical trial of the future" focusing on demographic inclusivity. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) introduced a novel model for public-private partnerships and data sharing. It successfully validated amyloid and Tau PET imaging, as well as CSF and plasma biomarkers, for diagnosing Alzheimer's disease. ADNI generated and disseminated vital data for designing AD clinical trials.

The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities, to ensure the success and maximize the impact of ADNI in advancing Alzheimer's disease (AD) research and clinical trials. It manages finances and develops policies for data sharing, publications using ADNI data, and access to ADNI biospecimens. The Core develops and executes pilot projects to guide future ADNI activities and identifies key innovative methods for inclusion in ADNI. For ADNI4, the Administrative Core collaborates with the Engagement, Clinical, and Biomarker Cores to develop and evaluate novel, digital methods and infrastructure for participant recruitment, screening, and assessment of participants. The goal of these efforts is to enroll 500 participants, including > 50% from underrepresented populations, 40% with mild cognitive impairment, and 80% with elevated AD biomarkers. This new approach also provides a unique opportunity to validate novel methods. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative (ADNI) Administrative Core oversees and coordinates all ADNI activities. The overall goal is to ensure ADNI's success and help design future Alzheimer's disease (AD) clinical trials. A key innovation is data sharing without embargo to maximize scientific impact. For ADNI4, novel, digital methods for recruitment and assessment were developed. New methods are designed to improve the participation of underrepresented populations.

INTRODUCTION:We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4). METHODS:Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. RESULTS:Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. DISCUSSION:Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. HIGHLIGHTS:A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

Importance:Black or African American (hereinafter, Black) and Hispanic or Latino/a/x (hereinafter, Latinx) adults are disproportionally affected by Alzheimer disease, but most research studies do not enroll adequate numbers of both of these populations. The Alzheimer's Disease Neuroimaging Initiative-3 (ADNI3) launched a diversity taskforce to pilot a multipronged effort to increase the study inclusion of Black and Latinx older adults. Objective:To describe and evaluate the culturally informed and community-engaged inclusion efforts to increase the screening and enrollment of Black and Latinx older adults in ADNI3. Design, Setting, and Participants:This cross-sectional study used baseline data from a longitudinal, multisite, observational study conducted from January 15, 2021, to July 12, 2022, with no follow-up. The study was conducted at 13 ADNI3 sites in the US. Participants included individuals aged 55 to 90 years without cognitive impairment and those with mild cognitive impairment or Alzheimer disease. Exposures:Efforts included (1) launch of an external advisory board, (2) changes to the study protocol, (3) updates to the digital prescreener, (4) selection and deployment of 13 community-engaged research study sites, (5) development and deployment of local and centralized outreach efforts, and (6) development of a community-science partnership board. Main Outcomes and Measures:Screening and enrollment numbers from centralized and local outreach efforts, digital advertisement metrics, and digital prescreener completion. Results:A total of 91 participants enrolled in the trial via centralized and local outreach efforts, of which 22 (24.2%) identified as Latinx and 55 (60.4%) identified as Black (median [IQR] age, 65.6 [IQR, 61.5-72.5] years; 62 women [68.1%]). This represented a 267.6% increase in the monthly rate of enrollment (before: 1.11 per month; during: 4.08 per month) of underrepresented populations. For the centralized effort, social media advertisements were run between June 1, 2021, and July 31, 2022, which resulted in 2079 completed digital prescreeners, of which 1289 met criteria for subsequent site-level screening. Local efforts were run between June 1, 2021, to July 31, 2022. A total of 151 participants underwent site-level screening (100 from local efforts, 41 from centralized efforts, 10 from other sources). Conclusions and Relevance:In this cross-sectional study of pilot inclusion efforts, a culturally informed, community-engaged approach increased the inclusion of Black and Latinx participants in an Alzheimer disease cohort study.

Background The Everyday Cognition (ECog) 12-item scale, a functional decline measurement, can distinguish dementia from cognitively unimpaired (CU). Limited data compare ECog-12 performance by raters (self vs. informant) and scoring systems (average numeric vs. categorical grouping) to differentiate cognitive statuses.Objectives To evaluate the performance of ECog-12 in differentiation cognitive statuses.Design A cross-sectional diagnostic test study.Setting and Participants Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study are analyzed. Participants were aged 55-90 years old divided into subgroups based on diagnostic criteria.Measurements We evaluated ECog-12 performance across different diagnostic groups, such as CU vs cognitive impairment (CI; mild cognitive impairment (MCI), and dementia), and the association between ECog-12 and CI. This procedure was repeated for self- and partner (informant)-reports. Additionally, types of ECog scores were also assessed, where an average ECog score was calculated (continuous numeric) as well as a categorical grouping ("any occasional declined" or "any consistently declined") based on item-level responses to ECog questions.Results ECog-12 cut-off scores of 1.36 (self-reported) and 1.45 (partner-reported) distinguish CU from CI with AUC 0.7 and 0.78, respectively. Adding a memory-concern question improved self-reported-ECog AUC to 0.79. Self- and partner-reported "consistently-declined" ECog-12 categorical grouping provided AUC 0.69 and 0.78. The study partner reported ECog-12 showed a greater association with CI than self-reported, with odds ratios of 35.45 and 8.79, respectively.Conclusion Study partner-reported ECog scores performed better than self-reported ECog-12 in differentiating cognitive statuses, and a higher study partner reported ECog score was a higher prognostic risk for CI. A memory concern question could enhance self-reported ECog-12 performance. This further emphasizes the need to obtain data from study partners for research and clinical practice.

INTRODUCTION:The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core was launched to advance Alzheimer's disease (AD) and AD-related dementia (ADRD) health equity research in underrepresented populations (URPs). We describe our evidence-based, scalable culturally informed, community-engaged research (CI-CER) model and demonstrate its preliminary success in increasing URP enrollment. METHODS:URPs include ethnoculturally minoritized, lower education (≤ 12 years), and rural populations. The CI-CER model includes: (1) culturally informed methodology (e.g., less restrictive inclusion/exclusion criteria, sociocultural measures, financial compensation, results disclosure, Spanish Language Capacity Workgroup) and (2) inclusive engagement methods (e.g., the Engagement Core team; Hub Sites; Community-Science Partnership Board). RESULTS:As of April 2024, 60% of ADNI-4 new in-clinic enrollees were from ethnoculturally or educationally URPs. This exceeds ADNI-4's ≥ 50% URP representation goal for new enrollees but may not represent final enrollment. DISCUSSION:Findings show a CI-CER model increases URP enrollment in AD/ADRD clinical research and has important implications for clinical trials to advance health equity. HIGHLIGHTS:The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) uses a culturally informed, community-engaged research (CI-CER) approach. The CI-CER approach is scalable and sustainable for broad, multisite implementation. ADNI-4 is currently exceeding its inclusion goals for underrepresented populations.

BACKGROUND The Everyday Cognition scale (ECog-39) scores are associated with future cognitive decline. We investigated whether the 12-item ECog (ECog-12), which is being collected in Alzheimer's Disease Neuroimaging Initiative (ADNI)4, can predict progression. METHODS Baseline self (PT)- and study partner (SP)-ECog-12 data were extracted from the 39-item version collected in the ADNI. Weibull analysis examined the relationship between baseline ECog-12 and future clinical progression (change in Clinical Dementia Rating Sum of Boxes [CDR-SB] scores and diagnostic conversion). RESULTS Higher PT- and SP-ECog-12 scores were associated with faster CDR-SB worsening, with hazard ratios in cognitively unimpaired (CU) 3.34 and 9.61, mild cognitive impairment (MCI) 1.44 and 2.82, and dementia 0.93 and 1.82. They were associated with conversion from CU to MCI 3.01 and 6.24 and MCI to dementia 1.61 and 3.07. DISCUSSIONS P-ECog-12 provided a higher prognostic value for predicting clinical progression, so this can help identify and monitor patients at risk in research and health-care settings. Highlights The 12-item Everyday Cognition scale (ECog-12) data obtained from both raters increased diagnostic conversion risk from cognitively unimpaired to mild cognitive impairment (MCI) and from MCI to dementia. ECog-12, rated by study partners, was associated with an increased risk of Clinical Dementia Rating Sum of Boxes worsening in all diagnostic groups. Our results provide novel information about the specific scoring outputs and rater types (participant vs. study partner) of ECog-12 that can facilitate screening, prioritization, and longitudinal monitoring of the clinical progression of participants in Alzheimer's Disease Neuroimaging Initiative 4 and other Alzheimer's disease clinical studies, clinical trials, and in health-care settings.

INTRODUCTION:This study aimed to understand whether older adults' longitudinal completion of assessments in an online Alzheimer's disease and related dementias (ADRD)-related registry is influenced by self-reported medical conditions. METHODS:Brain Health Registry (BHR) is an online cognitive aging and ADRD-related research registry that includes longitudinal health and cognitive assessments. Using logistic regressions, we examined associations between longitudinal registry completion outcomes and self-reported (1) number of medical conditions and (2) eight defined medical condition groups (cardiovascular, metabolic, immune system, ADRD, current psychiatric, substance use/abuse, acquired, other specified conditions) in adults aged 55+ (N = 23,888). Longitudinal registry completion outcomes were assessed by the completion of the BHR initial questionnaire (first questionnaire participants see at each visit) at least twice and completion of a cognitive assessment (Cogstate Brief Battery) at least twice. Models included ethnocultural identity, education, age, and subjective memory concern as covariates. RESULTS:We found that the likelihood of longitudinally completing the initial questionnaire was negatively associated with reporting a diagnosis of ADRD and current psychiatric conditions but was positively associated with reporting substance use/abuse and acquired medical conditions. The likelihood of longitudinally completing the cognitive assessment task was negatively associated with number of reported medical conditions, as well as with reporting cardiovascular conditions, ADRD, and current psychiatric conditions. Previously identified associations between ethnocultural identity and longitudinal assessment completion in BHR remained after accounting for the presence of medical conditions. DISCUSSION:This post hoc analysis provides novel, initial evidence that older adults' completion of longitudinal assessments in an online registry is associated with the number and types of participant-reported medical conditions. Our findings can inform future efforts to make online studies with longitudinal health and cognitive assessments more usable for older adults with medical conditions. The results need to be interpreted with caution due to selection biases, and the under-inclusion of minoritized communities.

Scalable tools to efficiently identify individuals likely to have cognitive impairment (CI) are critical in the Alzheimer’s disease and related dementias field. The Everyday Cognition scale (ECog) and its short form (ECog12) assess subjective cognitive and functional changes and are useful in predicting CI. This study aimed to compare the ability of the online ECog and the in-clinic ECog in distinguishing between CI and cognitively unimpaired (CU) individuals, and to evaluate the effectiveness of the ECog12 compared to the full ECog in an online setting. Participants were recruited from the Brain Health Registry (BHR; online) and Alzheimer’s Disease Neuroimaging Initiative (ADNI; in-clinic) with available clinical diagnoses. Ability of ECog and ECog12 (Self- and study partner [SP]-ECog) to discriminate CI from CU were calculated using Receiver Operating Characteristic (ROC) curves. Area under the ROC curves (AUCs) between BHR and ADNI were compared using the DeLong test, as were AUCs between ECog12 and ECog in BHR. Both online and in-clinic ECog effectively discriminated CI from CU, with no significant differences in AUCs (BHR Self-ECog AUC = 0.722 vs. ADNI Self-ECog AUC = 0.769, DeLong P = .06; BHR SP-ECog AUC = 0.818 vs. ADNI SP-ECog AUC = 0.840, DeLong P = .50). Comparison between online ECog and ECog12 showed no significant differences in AUCs (Self-ECog AUC = 0.722 vs. Self-ECog12 AUC = 0.709, DeLong P = .18). Online ECog, including the short-form ECog12, is as valid as in-clinic ECog for identifying clinically diagnosed CI, offering a cost-effective and accessible screening tool for large-scale online studies for identifying potential candidates for disease-modifying therapy.