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Objective: The purpose of this longitudinal study was to examine the prognostic value of subjective memory complaints in 156 cognitively intact community-dwelling older adults with a mean age of 83 years. Methods: Participants were assessed for subjective memory complaints, cognitive performance, functional status, and mood at annual evaluations with a mean follow-up of 4.5 years. Results: Subjective memory complaint at entry (n = 24) was not associated with impaired memory performance and did not predict memory decline or progression to incipient dementia. Memory complaints were inconsistent across examinations for 62% of participants who reported memory problems. Conclusion: Memory complaints by older adults are inconsistent over time. Memory complaints’ value as a research criterion for selecting people at risk of dementia is weak among community-dwelling older adults. Age, length of follow-up, and other population characteristics may affect the implication of self-reported memory problems.

Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. This poses a challenge in examining treatment effects since currently available approaches are often unable to detect cognitive and functional changes among asymptomatic individuals. Resultant small effect sizes require large sample sizes using biomarkers or secondary measures for randomized controlled trials (RCTs). Better assessment approaches and outcomes capable of capturing subtle changes during asymptomatic disease stages are needed.We aimed to develop a new approach to track changes in functional outcomes by using individual-specific distributions (as opposed to group-norms) of unobtrusive continuously monitored in-home data. Our objective was to compare sample sizes required to achieve sufficient power to detect prevention trial effects in trajectories of outcomes in two scenarios: (1) annually assessed neuropsychological test scores (a conventional approach), and (2) the likelihood of having subject-specific low performance thresholds, both modeled as a function of time.One hundred nineteen cognitively intact subjects were enrolled and followed over 3 years in the Intelligent Systems for Assessing Aging Change (ISAAC) study. Using the difference in empirically identified time slopes between those who remained cognitively intact during follow-up (normal control, NC) and those who transitioned to mild cognitive impairment (MCI), we estimated comparative sample sizes required to achieve up to 80% statistical power over a range of effect sizes for detecting reductions in the difference in time slopes between NC and MCI incidence before transition.Sample size estimates indicated approximately 2000 subjects with a follow-up duration of 4 years would be needed to achieve a 30% effect size when the outcome is an annually assessed memory test score. When the outcome is likelihood of low walking speed defined using the individual-specific distributions of walking speed collected at baseline, 262 subjects are required. Similarly for computer use, 26 subjects are required.Individual-specific thresholds of low functional performance based on high-frequency in-home monitoring data distinguish trajectories of MCI from NC and could substantially reduce sample sizes needed in dementia prevention RCTs.

Background: Social isolation and low levels of physical activity are strong drivers for frailty, which is linked to poor health outcomes and transition to long-term care. Frailty is multifactorial, and thus an integrated approach is needed to maintain older adults' health and well-being. Intergenerational programs represent a novel multifactorial approach to target frailty, social isolation and physical decline but these have not yet been rigorously tested in Australia. Here, we present the results of our pilot study which aimed to test the feasibility of a 10-week intergenerational program between older adults and preschool children., Methods: A non-randomised wait-listed controlled trial was conducted. Participants were allocated to either the intervention or wait-list control group. The intervention group received 10 weekly 2-h intergenerational sessions led by trained child educators; the control group continued with their usual routine and received their intergenerational program after the 10-week control period. All participants were assessed at baseline and 10 weeks. The primary outcome was the feasibility and acceptability of the program including measures of recruitment eligibility, adherence and effective data collection across the multiple domains important for frailty, including functional mobility and balance, grip strength, cognitive function, mood, social engagement, quality of life and concerns about falling., Results: Nineteen adults were included, with nine in the intervention and ten in the control group. A total of 42% of older adults screened were eligible, 75% of participants were present at each intervention session and the overall attrition rate was 21% (n = 4). The reasons for participant absence were primarily health-related. Missing data was minimal for the majority of assessments but more apparent for the cognitive testing where completion rates ranged from 53 to 79% for baseline tests and 73 to 100% for those who received follow-up testing., Conclusions: The high program compliance and low attrition show that a 10-week intergenerational program embedded in the local community, designed for community-living older adults and preschool children, is feasible and acceptable to older adults. Our next trial will test the efficacy of intergenerational programs in this setting., (© 2024. The Author(s).)

Background: Amyloid-beta (Aβ) plaque is a neuropathological hallmark of Alzheimer's disease (AD). As anti-amyloid monoclonal antibodies enter the market, predicting brain amyloid status is critical to determine treatment eligibility., Objective: To predict brain amyloid status utilizing machine learning approaches in the Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA) study., Design: ARMADA is a multisite study that implemented the National Institute of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in older adults with different cognitive ability levels (normal, mild cognitive impairment, early-stage dementia of the AD type)., Setting: Participants across various sites were involved in the ARMADA study for validating the NIHTB., Participants: 199 ARMADA participants had either PET or CSF information (mean age 76.3 ± 7.7, 51.3% women, 42.3% some or complete college education, 50.3% graduate education, 88.9% White, 33.2% with positive AD biomarkers)., Measurements: We used cognition, emotion, motor, sensation scores from NIHTB, and demographics to predict amyloid status measured by PET or CSF. We applied LASSO and random forest models and used the area under the receiver operating curve (AUROC) to evaluate the ability to identify amyloid positivity., Results: The random forest model reached AUROC of 0.74 with higher specificity than sensitivity (AUROC 95% CI:0.73 - 0.76, Sensitivity 0.50, Specificity 0.88) on the held-out test set; higher than the LASSO model (0.68 (95% CI:0.68 - 0.69)). The 10 features with the highest importance from the random forest model are: picture sequence memory, cognition total composite, cognition fluid composite, list sorting working memory, words-in-noise test (hearing), pattern comparison processing speed, odor identification, 2-minutes-walk endurance, 4-meter walk gait speed, and picture vocabulary. Overall, our model revealed the validity of measurements in cognition, motor, and sensation domains, in associating with AD biomarkers., Conclusion: Our results support the utilization of the NIH toolbox as an efficient and standardizable AD biomarker measurement that is better at identifying amyloid negative (i.e., high specificity) than positive cases (i.e., low sensitivity)., Competing Interests: On behalf of all authors, the corresponding author states that there is no conflict of interest.

Background: Measuring function with passive in-home sensors has the advantages of real-world, objective, continuous, and unobtrusive measurement. However, previous studies have focused on 1-person homes only, which limits their generalizability., Objective: This study aimed to compare the life space activity patterns of participants living alone with those of participants living as a couple and to compare people with mild cognitive impairment (MCI) with cognitively normal participants in both 1- and 2-person homes., Methods: Passive infrared motion sensors and door contact sensors were installed in 1- and 2-person homes with cognitively normal residents or residents with MCI. A home was classified as an MCI home if at least 1 person in the home had MCI. Time out of home (TOOH), independent life space activity (ILSA), and use of the living room, kitchen, bathroom, and bedroom were calculated. Data were analyzed using the following methods: (1) daily averages over 4 weeks, (2) hourly averages (time of day) over 4 weeks, or (3) longitudinal day-to-day changes., Results: In total, 129 homes with people living alone (n=27, 20.9%, MCI and n=102, 79.1%, no-MCI homes) and 52 homes with people living as a couple (n=24, 46.2%, MCI and n=28, 53.8%, no-MCI homes) were included with a mean follow-up of 719 (SD 308) days. Using all 3 analysis methods, we found that 2-person homes showed a shorter TOOH, a longer ILSA, and shorter living room and kitchen use. In MCI homes, ILSA was higher in 2-person homes but lower in 1-person homes. The effects of MCI status on other outcomes were only found when using the hourly averages or longitudinal day-to-day changes over time, and they depended on the household type (alone vs residing as a couple)., Conclusions: This study shows that in-home behavior is different when a participant is living alone compared to when they are living as a couple, meaning that the household type should be considered when studying in-home behavior. The effects of MCI status can be detected with in-home sensors, even in 2-person homes, but data should be analyzed on an hour-to-hour basis or longitudinally., (©Marijn Muurling, Wan-Tai M Au-Yeung, Zachary Beattie, Chao-Yi Wu, Hiroko Dodge, Nathaniel K Rodrigues, Sarah Gothard, Lisa C Silbert, Lisa L Barnes, Joel S Steele, Jeffrey Kaye. Originally published in JMIR Aging (https://aging.jmir.org), 11.10.2023.)

Introduction: We developed demographically-adjusted normative data for Spanish- and English-speaking Latinos on the Version 3.0 of the National Alzheimer's Coordinating Center Uniform Data Set Neuropsychological Battery (UDS3-NB)., Methods: Healthy Latino adults (N = 437) age 50-94 (191 Spanish- and 246 English-speaking) enrolled in Alzheimer's Disease Research Centers completed the UDS3-NB in their preferred language. Normative data were developed via multiple linear regression models on UDS3-NB raw scores stratified by language group with terms for demographic characteristics (age, years of formal education, and sex)., Results: Younger age and more years of education were associated with better performance on most tests in both language groups, with education being particularly influential on raw scores among Spanish-speakers. Sex effects varied across tests and language groups., Discussion: These normative data are a crucial step toward improving diagnostic accuracy of the UDS3-NB for neurocognitive disorders among Latinos in the United States and addressing disparities in Alzheimer's disease and related dementias., Highlights: We developed normative data on the UDS3-NB for Latinos in the US ages 50-94. Younger age and more years of education were linked to better raw scores in several cognitive tests. Education was particularly influential on raw scores among Spanish-speakers. Sex effects varied across tests and between English- and Spanish-speaking Latinos. These normative data might improve diagnostic accuracy of the UDS3-NB among Latinos., (© 2023 the Alzheimer's Association.)

Background: Describing changes in health and behavior that precede and follow a sentinel health event, such as a cancer diagnosis, is challenging because of the lack of longitudinal, objective measurements that are collected frequently enough to capture varying trajectories of change leading up to and following the event. A continuous passive assessment system that continuously monitors older adults' physical activity, weight, medication-taking behavior, pain, health events, and mood could enable the identification of more specific health and behavior patterns leading up to a cancer diagnosis and whether and how patterns change thereafter., Objective: In this study, we conducted a proof-of-concept retrospective analysis, in which we identified new cancer diagnoses in older adults and compared trajectories of change in health and behaviors before and after cancer diagnosis., Methods: Participants were 10 older adults (mean age 71.8, SD 4.9 years; 3/10, 30% female) with various self-reported cancer types from a larger prospective cohort study of older adults. A technology-agnostic assessment platform using multiple devices provided continuous data on daily physical activity via wearable sensors (actigraphy); weight via a Wi-Fi-enabled digital scale; daily medication-taking behavior using electronic Bluetooth-enabled pillboxes; and weekly pain, health events, and mood with online, self-report surveys., Results: Longitudinal linear mixed-effects models revealed significant differences in the pre- and postcancer trajectories of step counts (P<.001), step count variability (P=.004), weight (P<.001), pain severity (P<.001), hospitalization or emergency room visits (P=.03), days away from home overnight (P=.01), and the number of pillbox door openings (P<.001). Over the year preceding a cancer diagnosis, there were gradual reductions in step counts and weight and gradual increases in pain severity, step count variability, hospitalization or emergency room visits, and days away from home overnight compared with 1 year after the cancer diagnosis. Across the year after the cancer diagnosis, there was a gradual increase in the number of pillbox door openings compared with 1 year before the cancer diagnosis. There was no significant trajectory change from the pre- to post-cancer diagnosis period in terms of low mood (P=.60) and loneliness (P=.22)., Conclusions: A home-based, technology-agnostic, and multidomain assessment platform could provide a unique approach to monitoring different types of behavior and health markers in parallel before and after a life-changing health event. Continuous passive monitoring that is ecologically valid, less prone to bias, and limits participant burden could greatly enhance research that aims to improve early detection efforts, clinical care, and outcomes for people with cancer., (©Chao-Yi Wu, Deanne Tibbitts, Zachary Beattie, Hiroko Dodge, Jackilen Shannon, Jeffrey Kaye, Kerri Winters-Stone. Originally published in JMIR Formative Research (https://formative.jmir.org), 10.08.2023.)

Background: To respect people's preference for aging in place and control costs, many state Medicaid programs have enacted policies to expand home and community-based services as an alternative to nursing facility care. However, little is known about the use of Medicaid long-term services and supports (LTSS) at a national level, particularly among dual-eligible beneficiaries with Alzheimer's disease and related dementias (ADRD)., Methods: Using Medicare and Medicaid claims of 30 states from 2016, we focused on dual-eligible beneficiaries 65 years or older with ADRD and described their use of any form of LTSS and sub-types of LTSS (home-based, community-based, and nursing facility services) across states., Results: We found that 80.5% of dual-eligible beneficiaries with ADRD received some form of Medicaid LTSS in 2016. The most common LTSS setting was nursing facility (46.7%), followed by home (31.5%) and community (12.2%). There was sizeable state variation in the percentage of dual-eligible beneficiaries with ADRD who used any form of LTSS (ranging from 61% in Maine to 96% in Montana). The type of LTSS used also varied widely across states. For example, home-based service use ranged from 9% in Maine, Arizona, and South Dakota to 62% in Oregon. Nursing facility services were the most common type of LTSS in most states. However, home-based service use exceeded nursing facility use in Oregon, Alaska, and California., Conclusions: Our findings suggest substantially different use of LTSS across states among dual-eligible beneficiaries with ADRD. Given the importance of LTSS for this population and their families, a deeper understanding of state LTSS policies and other factors that contribute to wide state variation in LTSS use will be necessary to improve access to LTSS across states., (© 2022 The American Geriatrics Society.)

Clinical trials are increasingly focused on pre-manifest and early Alzheimer's disease (AD). Accurately predicting clinical progressions from normal to MCI or from MCI to dementia/AD versus non-progression is challenging. Accurate identification of symptomatic progressors is important to avoid unnecessary treatment and improve trial efficiency. Due to large inter-individual variability, biomarker positivity and comorbidity information are often insufficient to identify those destined to have symptomatic progressions. Using only clinical variables, we aimed to predict clinical progressions, estimating probabilities of progressions with a small set of variables selected by machine learning approaches. This work updates our previous work that was applied to the National Alzheimer's Coordinating Center (NACC) Uniform Data Set Version 2 (V2), by using the most recent version (V3) with additional analyses. We generated a user-friendly conversion probability calculator which can be used for effectively pre-screening trial participants., Competing Interests: No author has a conflict of interest in reporting this study.

Objectives: We aimed to examine the impact of COVID-19 pandemic-related stay-at-home orders on weekly reports of mood and activity before and during COVID-19 in a sample of older Veterans and their cohabitants., Methods: Urban and rural Veterans and their cohabitants living in the Pacific Northwest ≥62 years old were enrolled as part of the Collaborative Aging Research Using Technology initiative (n = 100, age = 71.2 ± 6.5, 41% women). Participants reported frequency of social activities (e.g., travel away), physical illness, and mood (blue mood and loneliness) via weekly online health forms., Results: A total of 2,441 weekly online health forms (OHFs) were collected from 100 participants. During the COVID-19 pandemic, blue mood (OR = 4.4, p < .0001) and loneliness (OR = 7.2, p < .0001) were significantly higher than before the pandemic, and travel away from home was significantly lower (OR = 0.5, p < .0001). Prevalence of blue mood and loneliness were not associated with rurality., Conclusions: The current study established that blue mood and loneliness were significantly more prevalent in older Veterans following COVID-19 stay-at-home orders regardless of rurality., Clinical Implications: The COVID-19 pandemic associated health precautions, while necessary to curb acute health risks, have created a unique situation that places vulnerable populations at increased risk of low mood.

Objective: Periventricular white matter hyperintensities (pvWMHs) are commonly observed on MRI in older individuals and are associated with cognitive and motor decline. The etiology of pvWMH remains unknown. Venous collagenosis has been implicated, which may also interfere with perivascular fluid flow leading to dilation of perivascular spaces (PVS). Here, we examine relationships between in vivo pvWMH volume and ex vivo morphological quantification of collagenosis and the PVS in veins and arteries., Methods: Brain tissue from 25 Oregon Alzheimer's Disease Research Center subjects was selected to cover the full range of WMH burden. Tissue from white matter abutting the ventricle was stained with Masson's trichrome and smooth muscle actin. An automated hue based algorithm identified and segmented vessel into collagenized vessel walls, lumen, and PVS. Multiple linear regressions with pvWMH volume as the dependent variable and either collagen thickness or PVS width were performed with covariates of vessel diameter, age at death, sex, and interval between MRI and death., Results: PVS width and collagen thickness were significantly correlated in both arteries (r = 0.21, p = 0.001) and veins (r = 0.23, p = 0.001). Increased venous collagen (p = 0.017) was a significant predictor of higher pvWMH burden while arterial collagen was not (p = 0.128). Neither PVS width in arteries (p = 0.937) nor veins (p = 0.133) predicted pvWMH burden., Interpretation: These findings are consistent with a model in which venous collagenosis mediates the relationship between vascular risk factors and pvWMH. This study confirms the importance of changes to the venous system in contributing to MRI white matter lesions commonly observed with advanced age. ANN NEUROL 2022;92:992-1000., (© 2022 The Authors. Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)

Federated learning is a distributed learning framework that is communication efficient and provides protection over participating users' raw training data. One outstanding challenge of federate learning comes from the users' heterogeneity, and learning from such data may yield biased and unfair models for minority groups. While adversarial learning is commonly used in centralized learning for mitigating bias, there are significant barriers when extending it to the federated framework. In this work, we study these barriers and address them by proposing a novel approach Federated Adversarial DEbiasing (FADE). FADE does not require users' sensitive group information for debiasing and offers users the freedom to opt-out from the adversarial component when privacy or computational costs become a concern. We show that ideally, FADE can attain the same global optimality as the one by the centralized algorithm. We then analyze when its convergence may fail in practice and propose a simple yet effective method to address the problem. Finally, we demonstrate the effectiveness of the proposed framework through extensive empirical studies, including the problem settings of unsupervised domain adaptation and fair learning. Our codes and pre-trained models are available at: https://github.com/illidanlab/FADE.

Introduction: Non-pharmacological treatments (NPTs) have the potential to improve meaningful outcomes for older people at risk of, or living with dementia, but research often lacks methodological rigor and continues to produce mixed results., Methods: In the current position paper, experts in NPT research have specified treatment targets, aims, and ingredients using an umbrella framework, the Rehabilitation Treatment Specification System., Results: Experts provided a snapshot and an authoritative summary of the evidence for different NPTs based on the best synthesis efforts, identified main gaps in knowledge and relevant barriers, and provided directions for future research. Experts in trial methodology provide best practice principles and recommendations for those working in this area, underscoring the importance of prespecified protocols., Discussion: We conclude that the evidence strongly supports various NPTs in relation to their primary targets, and discuss opportunities and challenges associated with a unifying theoretical framework to guide future efforts in this area., (© 2020 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)

Dementia is a risk factor for epilepsy. While seizures have a well-established association with Alzheimer's disease (AD), their association with dementia with Lewy bodies (DLB) is not established. We utilized the National Alzheimer's Coordinating Centers' Uniform Data Set (NACC-UDS V1-3) to analyze occurrence of seizures in DLB and seizure occurrence associations with mortality. We excluded subjects with conventional seizure risk factors. Seizure occurrence was noted in 36 subjects (2.62%) out of 1376 subjects with DLB. Among 500 subjects with pathologically confirmed DLB, seizure occurrence was documented in 19 (3.8%) subjects. Half of the subjects had onset of seizures three years before or after DLB diagnosis. Two-year mortality for subjects with DLB with seizures was high at 52.8% but no increased risk was noted as compared with subjects with DLB without seizures. More prospective and long-term longitudinal studies are needed to clarify relationships between DLB, seizure occurrence, and risk of increased mortality., Competing Interests: Declaration of competing interest Rohit Marawar, Nicole Wakim, Roger Albin, and Hiroko Dodge have no conflicts of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Background: The current clinical trial assessment methodology relies on a combination of self-report measures, cognitive and physical function tests, and biomarkers. This methodology is limited by recall bias and recency effects in self-reporting and by assessments that are brief, episodic, and clinic based. Continuous monitoring of ecologically valid measures of cognition and daily functioning in the community may provide a more sensitive method to detect subtle, progressive changes in patients with cognitive impairment and dementia., Objective: This study aimed to present an alternative trial approach using a home-based sensing and computing system to detect changes related to common treatments employed in Alzheimer disease (AD). This paper introduces an ongoing study that aims to determine the feasibility of capturing sensor-based data at home and to compare the sensor-based outcomes with conventional outcomes. We describe the methodology used in the assessment protocol and present preliminary results of feasibility measures and examples of data related to medication-taking behavior, activity levels, and sleep., Methods: The EVALUATE-AD (Ecologically Valid, Ambient, Longitudinal and Unbiased Assessment of Treatment Efficacy in Alzheimer's Disease) trial is a longitudinal naturalistic observational cohort study recruiting 30 patients and 30 spouse coresident care partners. Participants are monitored continuously using a home-based sensing and computing system for up to 24 months. Outcome measures of the automated system are compared with conventional clinical outcome measures in AD. Acceptance of the home system and protocol are assessed by rates of dropout and protocol adherence. After completion of the study monitoring period, a composite model using multiple functional outcome measures will be created that represents a behavioral-activity signature of initiating or discontinuing AD-related medications, such as cholinesterase inhibitors, memantine, or antidepressants., Results: The home-based sensing and computing system has been well accepted by individuals with cognitive impairment and their care partners. Participants showed good adherence to the completion of a weekly web-based health survey. Daily activity, medication adherence, and total time in bed could be derived from algorithms using data from the sensing and computing system. The mean monitoring time for current participants was 14.6 months. Medication adherence, as measured with an electronic pillbox, was 77% for participants taking AD-related medications., Conclusions: Continuous, home-based assessment provides a novel approach to test the impact of new or existing dementia treatments generating objective, clinically meaningful measures related to cognition and everyday functioning. Combining this approach with the current clinical trial methodology may ultimately reduce trial durations, sample size needs, and reliance on a clinic-based assessment., International Registered Report Identifier (irrid): DERR1-10.2196/17603., (©Neil William Douglas Thomas, Zachary Beattie, Jennifer Marcoe, Kirsten Wright, Nicole Sharma, Nora Mattek, Hiroko Dodge, Katherine Wild, Jeffrey Kaye. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 27.05.2020.)

Background: Although there are known clinical measures that may be associated with risk of future falls in older adults, we are still unable to predict when the fall will happen. Our objective was to determine whether unobtrusive in-home assessment of walking speed can detect a future fall., Method: In both ISAAC and ORCATECH Living Laboratory studies, a sensor-based monitoring system has been deployed in the homes of older adults. Longitudinal mixed-effects regression models were used to explore trajectories of sensor-based walking speed metrics in those destined to fall versus controls over time. Falls were captured during a 3-year period., Results: We observed no major differences between those destined to fall (n = 55) and controls (n = 70) at baseline in clinical functional tests. There was a longitudinal decline in median daily walking speed over the 3 months before a fall in those destined to fall when compared with controls, p < .01 (ie, mean walking speed declined 0.1 cm s-1 per week). We also found prefall differences in sensor-based walking speed metrics in individuals who experienced a fall: walking speed variability was lower the month and the week just before the fall compared with 3 months before the fall, both p < .01., Conclusions: While basic clinical tests were not able to differentiate who will prospectively fall, we found that significant variations in walking speed metrics before a fall were measurable. These results provide evidence of a potential sensor-based risk biomarker of prospective falls in community living older adults., (© The Author(s) 2019. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Introduction: Clinically relevant information can go uncaptured in the conventional scoring of a verbal fluency test. We hypothesize that characterizing the temporal aspects of the response through a set of time related measures will be useful in distinguishing those with MCI from cognitively intact controls. Methods: Audio recordings of an animal fluency test administered to 70 demographically matched older adults (mean age 90.4 years), 28 with mild cognitive impairment (MCI) and 42 cognitively intact (CI) were professionally transcribed and fed into an automatic speech recognition (ASR) system to estimate the start time of each recalled word in the response. Next, we semantically cluster participant generated animal names and through a novel set of time-based measures, we characterize the semantic search strategy of subjects in retrieving words from animal name clusters. This set of time-based features along with standard count-based features (e.g., number of correctly retrieved animal names) were then used in a machine learning algorithm trained for distinguishing those with MCI from CI controls. Results: The combination of both count-based and time-based features, automatically derived from the test response, achieved 77% on AUC-ROC of the support vector machine (SVM) classifier, outperforming the model trained only on the raw test score (AUC, 65%), and well above the chance model (AUC, 50%). Conclusion: This approach supports the value of introducing time-based measures to the assessment of verbal fluency in the context of this generative task differentiating subjects with MCI from those with intact cognition., (Copyright © 2020 Chen, Asgari, Gale, Wild, Dodge and Kaye.)

In the past decade a large body of evidence has accumulated on risk factors for dementia, primarily from Europe and North America. Drawing on recent integrative reviews and a consensus workshop, the International Research Network on Dementia Prevention developed a consensus statement on priorities for future research. Significant gaps in geographical location, representativeness, diversity, duration, mechanisms, and research on combinations of risk factors were identified. Future research to inform dementia risk reduction should fill gaps in the evidence base, take a life-course, multi-domain approach, and inform population health approaches that improve the brain-health of whole communities.

Frailty has been established as a risk factor for falls, and prefrailty also seems a risk; however, few studies have focused on the association between falls and each of the five components of frailty proposed by Fried. In the present study, we sought to elucidate the association between prefrailty and falls, and moreover, the association of frailty component with falls. Participants were community-dwelling older people who had cognitive complaints but not dementia (N = 447, male 54.6%). Prefrailty was defined as exhibiting one or two of the five Fried criteria. Frail individuals were excluded. Background characteristics were compared between the prefrail and robust groups, and multiple regression analysis was performed to investigate the associations between fall history within the past year and factors that were significantly different between the groups. We also performed logistic regression analysis with adjustment for age, education and gender to assess associations with frailty components. We found that prefrailty was associated with fall history. Depressed mood was also significantly associated with fall history. Among the five frailty criteria, exhaustion was significantly associated with falls. Prefrailty, especially the criteria of exhaustion, and depressed mood were associated with fall history., (© 2019 John Wiley & Sons Ltd.)

Background: Cerebrovascular disease is a common cause of dementia in older adults, and potentially preventable with early intervention. Oxylipins are produced from the oxidation of long-chain polyunsaturated fatty acids (PUFA) possessing potent vascular effects. Oxylipins generated from the cytochrome P450 pathway are enzymatically converted to diols by soluble epoxide hydrolase (sEH); sEH products have been associated with small vessel ischemic disease. Little is known about oxylipins' impact on markers of dementia risk., Objective: An exploratory examination of the association between omega-6 and omega-3 derived oxylipins, brain MRI, and cognition., Methods: Thirty-seven non-demented participants with controlled hypertension (mean age 65.6 years) were enrolled in a dementia prevention study investigating fish oil and lipoic acid on preserving cognitive function. Baseline associations between plasma oxylipins, white matter hyperintensity (WMH), and Trails-B were examined using linear regression. P450-derived diol/epoxide ratio was an indirect measure of sEH activity., Results: Omega-6 derived 9-HODE was associated with increased WMH (p = 0.017) and reduced grey matter volume (p = 0.02). Omega-6 P450-derived diol/epoxide ratio 9,10-DiHOME/9,10-EpOME was associated with increased WMH (p = 0.035) and poorer performance on Trails-B (p = 0.05); ratio14,15-DHET/14,15-EET was associated with increased WMH (p = 0.045). Omega-3 P450-derived diol/epoxide ratio 19,20-DiHDPE/19,20-EpDPE was associated with increased WMH (p = 0.04) and poorer performance on Trails-B (p = 0.04). Arachidonic acid was associated with better performance on Trails-B (p = 0.012); Omega-3 derived 16,17-EpDPE was associated with decreased WMH (p = 0.005)., Conclusions: With the exception of arachidonic acid, it was specific oxylipin products, not their parent PUFAs, that were associated with unfavorable and favorable MRI and cognitive markers of dementia risk.

Background: Current conventional cognitive assessments are limited in their efficiency and sensitivity, often relying on a single score such as the total correct items. Typically, multiple features of response go uncaptured., Objectives: We aim to explore a new set of automatically derived features from the Digit Span (DS) task that address some of the drawbacks in the conventional scoring and are also useful for distinguishing subjects with Mild Cognitive Impairment (MCI) from those with intact cognition., Methods: Audio-recordings of the DS tests administered to 85 subjects (22 MCI and 63 healthy controls, mean age 90.2 years) were transcribed using an Automatic Speech Recognition (ASR) system. Next, five correctness measures were generated from Levenshtein distance analysis of responses: number correct, incorrect, deleted, inserted, and substituted words compared to the test item. These per-item features were aggregated across all test items for both Forward Digit Span (FDS) and Backward Digit Span (BDS) tasks using summary statistical functions, constructing a global feature vector representing the detailed assessment of each subject's response. A support vector machine classifier distinguished MCI from cognitively intact participants., Results: Conventional DS scores did not differentiate MCI participants from controls. The automated multi-feature DS-derived metric achieved 73% on AUC-ROC of the SVM classifier, independent of additional clinical features (77% when combined with demographic features of subjects); well above chance, 50%., Conclusion: Our analysis verifies the effectiveness of introduced measures, solely derived from the DS task, in the context of differentiating subjects with MCI from those with intact cognition., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Introduction: Conventional Z-scores are generated by subtracting the mean and dividing by the standard deviation. More recent methods linearly correct for age, sex, and education, so that these "adjusted" Z-scores better represent whether an individual's cognitive performance is abnormal. Extreme negative Z-scores for individuals relative to this normative distribution are considered indicative of cognitive deficiency., Methods: In this article, we consider nonlinear shape constrained additive models accounting for age, sex, and education (correcting for nonlinearity). Additional shape constrained additive models account for varying standard deviation of the cognitive scores with age (correcting for heterogeneity of variance)., Results: Corrected Z-scores based on nonlinear shape constrained additive models provide improved adjustment for age, sex, and education, as indicated by higher adjusted-R 2 ., Discussion: Nonlinearly corrected Z-scores with respect to age, sex, and education with age-varying residual standard deviation allow for improved detection of non-normative extreme cognitive scores., (© 2019 Published by Elsevier Inc. on behalf of the Alzheimer's Association.)

Background: Understanding contributions of different brain pathologies to domain-specific cognitive trajectories in the oldest old is crucial to guide future intervention studies., Methods: Two-hundred-twenty Oregon Alzheimer's Disease Center research participants who were cognitively intact at entry were followed on average for 7.3 years with annual neuropsychological testing until death (mean age, 93.7 y) and autopsy. Mixed effects models examined the relationship between trajectories in memory, verbal fluency, and mini-mental state examination (MMSE) and pathology (neurofibrillary tangles, neuritic plaques, gross infarcts, hippocampal sclerosis, Lewy bodies, APOE genotype, age at death, and years of education). The association between the MMSE trajectory and pathologic variables were examined using a Poisson model with MMSE errors as outcomes given the nonlinear distribution of MMSE scores., Results: Memory trajectory was associated with the APOε4 allele (P=0.006). Verbal fluency trajectory was associated with gross infarcts (P=0.008). MMSE trajectory was associated with high Braak scores (P=0.03), gross infarcts (P<0.0001), hippocampal sclerosis (P=0.003), moderate neuritic plaques (P=0.04), and the APOε4 allele (P=0.02)., Conclusions: The association between trajectory of decline in global cognitive scores and multiple brain pathologies highlights the importance of accounting for comorbid pathologies in therapeutic trials aimed at one specific pathology in the oldest old. Only the APOε4 allele showed an association with memory decline, despite accounting for Alzheimer's disease pathology, suggesting that APOE may be involved in mechanisms beyond amyloid metabolism in its role in memory. Further studies are needed to examine the role of APOE in brain aging.

T1-weighted MRI has been extensively used to extract imaging biomarkers and build classification models for differentiating Alzheimer's disease (AD) patients from healthy controls, but only recently have brain connectome networks derived from diffusion-weighted MRI been used to model AD progression and various stages of disease such as mild cognitive impairment (MCI). MCI, as a possible prodromal stage of AD, has gained intense interest recently, since it may be used to assess risk factors for AD. Little work has been done to combine information from both white matter and gray matter, and it is unknown how much classification power the diffusion-weighted MRI-derived structural connectome could provide beyond information available from T1-weighted MRI. In this paper, we focused on investigating whether diffusion-weighted MRI-derived structural connectome can improve differentiating healthy controls subjects from those with MCI. Specifically, we proposed a novel feature-ranking method to build classification models using the most highly ranked feature variables to classify MCI with healthy controls. We verified our method on two independent cohorts including the second stage of Alzheimer's Disease Neuroimaging Initiative (ADNI2) database and the National Alzheimer's Coordinating Center (NACC) database. Our results indicated that 1) diffusion-weighted MRI-derived structural connectome can complement T1-weighted MRI in the classification task; 2) the feature-rank method is effective because of the identified consistent T1-weighted MRI and network feature variables on ADNI2 and NACC. Furthermore, by comparing the top-ranked feature variables from ADNI2, NACC, and combined dataset, we concluded that cross-validation using independent cohorts is necessary and highly recommended.

Introduction: Racial and ethnic groups are under-represented among research subjects who assent to brain donation in Alzheimer disease research studies. There has been little research on this important topic. Although there are some studies that have investigated the barriers to brain donation among African American study volunteers, there is no known research on the factors that influence whether or not Asians or Latinos are willing to donate their brains for research., Methods: African American, Caucasian, Asian, and Latino research volunteers were surveyed at 15 Alzheimer Disease Centers to identify predictors of willingness to assent to brain donation., Results: Positive predictors included older age, Latino ethnicity, understanding of how the brain is used by researchers, and understanding of what participants need to do to ensure that their brain will be donated. Negative predictors included African/African American race, belief that the body should remain whole at burial, and concern that researchers might not be respectful of the body during autopsy., Discussion: The predictive factors identified in this study may be useful for researchers seeking to increase participation of diverse ethnic groups in brain donation.

Introduction: Trials in Alzheimer's disease are increasingly focusing on prevention in asymptomatic individuals. We hypothesized that indicators of mild cognitive impairment (MCI) may be present in the content of spoken language in older adults and be useful in distinguishing those with MCI from those who are cognitively intact. To test this hypothesis, we performed linguistic analyses of spoken words in participants with MCI and those with intact cognition participating in a clinical trial., Methods: Data came from a randomized controlled behavioral clinical trial to examine the effect of unstructured conversation on cognitive function among older adults with either normal cognition or MCI (ClinicalTrials.gov: NCT01571427). Unstructured conversations (but with standardized preselected topics across subjects) were recorded between interviewers and interviewees during the intervention sessions of the trial from 14 MCI and 27 cognitively intact participants. From the transcription of interviewees recordings, we grouped spoken words using Linguistic Inquiry and Word Count (LIWC), a structured table of words, which categorizes 2500 words into 68 different word subcategories such as positive and negative words, fillers, and physical states. The number of words in each LIWC word subcategory constructed a vector of 68 dimensions representing the linguistic features of each subject. We used support vector machine and random forest classifiers to distinguish MCI from cognitively intact participants., Results: MCI participants were distinguished from those with intact cognition using linguistic features obtained by LIWC with 84% classification accuracy which is well above chance 60%., Discussion: Linguistic analyses of spoken language may be a powerful tool in distinguishing MCI subjects from those with intact cognition. Further studies to assess whether spoken language derived measures could detect changes in cognitive functions in clinical trials are warrented.

Background: Driving is a key functional activity for many older adults, and changes in routine driving may be associated with emerging cognitive decline due to early neurodegenerative disease. Current methods for assessing driving such as self-report are inadequate for identifying and monitoring subtle changes in driving patterns that may be the earliest signals of functional change in developing mild cognitive impairment (MCI)., Objective: This proof of concept study aimed to establish the feasibility of continuous driving monitoring in a sample of cognitively normal and MCI older adults for an average of 206 days using an unobtrusive driving sensor and demonstrate that derived sensor-based driving metrics could effectively discriminate between MCI and cognitively intact groups., Methods: Novel objective driving measures derived from 6 months of routine driving monitoring were examined in older adults with intact cognition (n = 21) and MCI (n = 7) who were enrolled in the Oregon Center for Aging and Technology (ORCATECH) longitudinal assessment program., Results: Unobtrusive continuous monitoring of older adults' routine driving using a driving sensor was feasible and well accepted. MCI participants drove fewer miles and spent less time on the highway per day than cognitively intact participants. MCI drivers showed less day-to-day fluctuations in their driving habits than cognitively intact drivers., Conclusion: Sensor-based driving measures are objective, unobtrusive, and can be assessed every time a person drives his or her vehicle to identify clinically meaningful changes in daily driving. This novel methodology has the potential to be useful for the early detection and monitoring of changes in daily functioning within individuals.

Objective: To understand the longitudinal relationship between loneliness and isolation., Method: Participants included 5,870 adults 65 years and older (M = 72.89 ± 5.59 years) from the first 5 years of the Cardiovascular Health Study. Loneliness was assessed using a dichotomized loneliness question. Social isolation was assessed using six items from the Lubben Social Network Scale. Yearly life events were included to assess abrupt social network changes. Mixed effects logistic regression was employed to analyze the relationship between isolation and loneliness., Results: Higher levels of social isolation were associated with higher odds of loneliness, as was an increase (from median) in level of social isolation. Life events such as a friend dying were also associated with increased odds of loneliness., Discussion: These results suggest that average level of isolation and increases in the level of isolation are closely tied to loneliness, which has implications for future assessment or monitoring of loneliness in older adult populations., (© The Author(s) 2015.)

Background: Changes in criteria and differences in populations studied and methodology have produced a wide range of prevalence estimates for mild cognitive impairment (MCI)., Methods: Uniform criteria were applied to harmonized data from 11 studies from USA, Europe, Asia and Australia, and MCI prevalence estimates determined using three separate definitions of cognitive impairment., Results: The published range of MCI prevalence estimates was 5.0%-36.7%. This was reduced with all cognitive impairment definitions: performance in the bottom 6.681% (3.2%-10.8%); Clinical Dementia Rating of 0.5 (1.8%-14.9%); Mini-Mental State Examination score of 24-27 (2.1%-20.7%). Prevalences using the first definition were 5.9% overall, and increased with age (P < .001) but were unaffected by sex or the main races/ethnicities investigated (Whites and Chinese). Not completing high school increased the likelihood of MCI (P ≤ .01)., Conclusion: Applying uniform criteria to harmonized data greatly reduced the variation in MCI prevalence internationally.

Traditionally, assessment of functional and cognitive status of individuals with dementia occurs in brief clinic visits during which time clinicians extract a snapshot of recent changes in individuals' health. Conventionally, this is done using various clinical assessment tools applied at the point of care and relies on patients' and caregivers' ability to accurately recall daily activity and trends in personal health. These practices suffer from the infrequency and generally short durations of visits. Since 2004, researchers at the Oregon Center for Aging and Technology (ORCATECH) at the Oregon Health and Science University have been working on developing technologies to transform this model. ORCATECH researchers have developed a system of continuous in-home monitoring using pervasive computing technologies that make it possible to more accurately track activities and behaviors and measure relevant intra-individual changes. We have installed a system of strategically placed sensors in over 480 homes and have been collecting data for up to 8 years. Using this continuous in-home monitoring system, ORCATECH researchers have collected data on multiple behaviors such as gait and mobility, sleep and activity patterns, medication adherence, and computer use. Patterns of intra-individual variation detected in each of these areas are used to predict outcomes such as low mood, loneliness, and cognitive function. These methods have the potential to improve the quality of patient health data and in turn patient care especially related to cognitive decline. Furthermore, the continuous real-world nature of the data may improve the efficiency and ecological validity of clinical intervention studies.

Physical performance measures predict health and function in older populations. Walking speed in particular has consistently predicted morbidity and mortality. However, single brief walking measures may not reflect a person's typical ability. Using a system that unobtrusively and continuously measures walking activity in a person's home we examined walking speed metrics and their relation to function. In 76 persons living independently (mean age, 86) we measured every instance of walking past a line of passive infra-red motion sensors placed strategically in their home during a four-week period surrounding their annual clinical evaluation. Walking speeds and the variance in these measures were calculated and compared to conventional measures of gait, motor function and cognition. Median number of walks per day was 18±15. Overall mean walking speed was 61±17 cm/s. Characteristic fast walking speed was 96 cm/s. Men walked as frequently and fast as women. Those using a walking aid walked significantly slower and with greater variability. Morning speeds were significantly faster than afternoon/evening speeds. In-home walking speeds were significantly associated with several neuropsychological tests as well as tests of motor performance. Unobtrusive home walking assessments are ecologically valid measures of walking function. They provide previously unattainable metrics (periodicity, variability, range of minimum and maximum speeds) of everyday motor function., (Published by Elsevier B.V.)

Objectives: To describe a longitudinal community cohort study, Intelligent Systems for Assessing Aging Changes, that has deployed an unobtrusive home-based assessment platform in many seniors homes in the existing community., Methods: Several types of sensors have been installed in the homes of 265 elderly persons for an average of 33 months. Metrics assessed by the sensors include total daily activity, time out of home, and walking speed. Participants were given a computer as well as training, and computer usage was monitored. Participants are assessed annually with health and function questionnaires, physical examinations, and neuropsychological testing., Results: Mean age was 83.3 years, mean years of education was 15.5, and 73% of cohort were women. During a 4-week snapshot, participants left their home twice a day on average for a total of 208 min per day. Mean in-home walking speed was 61.0 cm/s. Participants spent 43% of days on the computer averaging 76 min per day., Discussion: These results demonstrate for the first time the feasibility of engaging seniors in a large-scale deployment of in-home activity assessment technology and the successful collection of these activity metrics. We plan to use this platform to determine if continuous unobtrusive monitoring may detect incident cognitive decline.

Objective: To determine whether white matter hyperintensity (WMH) progression rate is a better predictor of cognitive impairment risk than baseline WMH volume in healthy elderly individuals., Method: Ninety-eight cognitively intact elderly subjects were followed in the Oregon Brain Aging Study. Forty-nine had at least 3 brain MRIs and annual cognitive and neurologic assessments until diagnosed with persistent cognitive impairment (PCI). Brain, ventricular CSF (vCSF), intracranial volume (ICV), hippocampus, total WMH, periventricular (PV) WMH, and subcortical WMH volumes were measured. Cox proportional hazards survival analyses were used to assess cognitive impairment risk., Results: After adjusting for age, apolipoprotein E4 status, incident hypertension, ICV, entry Mini-Mental State Examination, baseline hippocampus, and both baseline vCSF volume and rate of vCSF volume change, increased progression of total WMH volume (hazard ratio [HR] 1.84, 95% confidence interval [CI] 1.3-2.7, p = 0.0007) and PV WMH volume (HR 1.94, 95% CI 1.3-3.1, p = 0.001) conferred higher risk of PCI, whereas baseline WMH volumes did not. Every 1 mL/y increase in PV WMH volume was associated with a 94% increased risk of PCI., Conclusion: Progression of total and periventricular (PV) white matter hyperintensity (WMH) volumes are better predictors of persistent cognitive impairment (PCI) than baseline WMH burden. Greater PV WMH burden progression is associated with the development of PCI, a potential precursor to Alzheimer or vascular dementia. Identification of factors that decrease WMH accumulation over time is needed to maintain cognitive health in our growing elderly population.

The brain maintains high levels of ascorbic acid (AA) despite a concentration gradient favoring diffusion from brain to peripheral tissues. Dietary antioxidants, including AA, appear to modify the risk of Alzheimer's disease (AD). The objective of this study was to test the hypothesis that neurodegeneration in AD is modified by brain levels of AA. Thirty-two patients with mild to moderate AD participated in a biomarker study involving standardized clinical assessments over one year. Cerebrospinal fluid (CSF) and serum were collected at baseline for AA and albumin content. Cognitive measures were collected at baseline and one year. CSF and plasma AA failed to predict cognitive decline independently, however, CSF: plasma AA ratio did. After adding CSF Albumin Index (an established marker of blood-brain barrier integrity) to the regression models the effect of CSF: plasma AA ratio as a predictor of cognitive decline was weakened. CSF: plasma AA ratio predicts rate of decline in AD. This relationship may indicate that the CSF: plasma AA ratio is an index of AA availability to the brain or may be an artifact of a relationship between blood-brain barrier impairment and neurodegeneration.

Objective: The authors sought to identify patterns and associations of prescription and over-the-counter sedative-hypnotic use in an older, rural, blue-collar, community-based cohort in southwestern Pennsylvania over 10 years., Methods: A group of 1,627 individuals age 65 and over were recruited and assessed during 1987-1989 and re-assessed during approximately biennial waves. Data included sleep medications, demographics, depressive symptoms, sleep complaints, and cognitive functioning (Mini-Mental State Exam [MMSE])., Results: At Waves 1 through 5, the mean age of the cohort increased from 73.4 to 80.5 years. Use of prescription sedative-hypnotics (primarily benzodiazepines) increased from 1.8% to 3.1%, and over-the-counter sedative-hypnotic use (primarily diphenhydramine) increased from 0.4% to 7.6%. At Wave 5 (1996-1998), 8.17% of the sample reported using diphenhydramine as a sleep aid. After adjusting for age and sex, diphenhydramine use was associated with higher education and more depressive symptoms, the latter becoming nonsignificant after controlling for initial insomnia. MMSE became significantly associated with diphenhydramine use when 143 subjects with dementia were excluded from the analysis., Conclusion: As the cohort aged, prescription sedative-hypnotic use remained relatively stable, whereas over-the-counter sedative use, principally diphenhydramine, increased substantially. The association of this drug with cognitive impairment in persons without dementia highlights its potential for causing adverse reactions in older adults.

Background: Serum anticholinergic activity (SAA), as measured by a radioreceptor assay, quantifies a person's overall anticholinergic burden caused by all drugs and their metabolites. In several small geriatric patient groups, SAA has been associated with cognitive impairment or frank delirium. To our knowledge, there has not yet been any systematic study of the prevalence of SAA and its effect on cognition in a community-based population., Methods: Serum anticholinergic activity was measured in 201 subjects who were randomly selected among the participants in an epidemiological community study, based on their age and sex. Cognitive performance was assessed with use of the Mini-Mental State Examination. The association between SAA and cognitive performance was examined using a univariate analysis and a multiple logistic regression model, adjusting for age, sex, educational level, and number of medications., Results: Serum anticholinergic activity was detectable in 180 (89.6%) participants (range, 0.50-5.70 pmol/mL). Univariate testing showed a significant association between SAA and Mini-Mental State Examination scores. Logistic regression analysis indicated that subjects with SAA at or above the sample's 90th percentile (ie, SAA >/=2.80 pmol/mL) were 13 times (odds ratio, 1.08-152.39) more likely than subjects with undetectable SAA to have a Mini-Mental State Examination score of 24 (the sample's 10th percentile) or below., Conclusions: To our knowledge, this is the largest analysis of SAA and the first to examine its extent and relationship with cognitive performance in a community sample. Its results suggest that SAA can be detected in most older persons in the community and confirm that even low SAA is associated with cognitive impairment.

Over 10 years, a community-based sample age 65> or = years, with a starting cohort size of 1,206, was assessed biennially with the Mini-Mental State Exam; the Consortium to Establish a Registry for Alzheimer's Disease battery; Immediate and Delayed Recall of a Story; Verbal Fluency for P and S, Fruits and Animals; Clock Drawing; Temporal Orientation; and Trail Making tests. We report distributions of scores over time, at each wave, in (a) all individuals who were assessed at that wave, whether or not they participated in all waves, and (b) the Survivor subgroup of 425 participants who completed all tests at all 5 waves. Scores and factor structures remained remarkably stable over the study period. The most marked decline over time was seen on the Trail Making tests. As the survivors are de facto a largely healthy and motivated group, their data can be considered population-based healthy norms and may serve as a reference for other studies conducting repeated evaluations using the same tests.

There is a shortage of adequate screening instruments for dementia in poorly educated populations and non-English-speaking groups. An epidemiological survey was conducted in a population-based, largely illiterate, sample of 5,126 individuals aged 55 and older in 28 villages in the rural community of Ballabgarh in northern India. All participants were administered a general mental status test, the Hindi Mental State Examination (HMSE), and a brief battery of neuropsychological tests. Their informants answered a questionnaire assessing functional ability, the Everyday Abilities Scale for India (EASI). Six hundred thirty-two participants underwent clinical diagnostic evaluation for dementia. We investigated whether the sensitivity, specificity, and predictive value for dementia of the mental status test could be improved by the addition of the brief neuropsychological test battery or the functional questionnaire, comparing the instruments alone and in combination. In participants who could be tested cognitively, the HMSE, the neuropsychological battery, and EASI had sensitivities of 81.3%, 81.3%, and 62.5%, respectively, with specificities of 60.2%, 74.5%, and 89.7%, respectively. The combination of all three was 93.8% sensitive and 41.8% specific. The sensitivity of the HMSE alone was nonsignificantly improved by the addition of either the EASI or the neuropsychological battery, whereas its specificity was significantly decreased by either addition. An advantage of the EASI was that it could also be administered to informants of subjects who were cognitively untestable. In this largely illiterate community, with a low prevalence of dementia, the combination of cognitive tests and a functional ability questionnaire had substantial value for population screening.