Linguistic-based Mild Cognitive Impairment detection using Informative Loss.

This paper presents a deep learning method using Natural Language Processing (NLP) techniques, to distinguish between Mild Cognitive Impairment (MCI) and Normal Cognitive (NC) conditions in older adults. We propose a framework that analyzes transcripts generated from video interviews collected within the I-CONECT study project, a randomized controlled trial aimed at improving cognitive functions through video chats. Our proposed NLP framework consists of two Transformer-based modules, namely Sentence Embedding (SE) and Sentence Cross Attention (SCA). First, the SE module captures contextual relationships between words within each sentence. Subsequently, the SCA module extracts temporal features from a sequence of sentences. This feature is then used by a Multi-Layer Perceptron (MLP) for the classification of subjects into MCI or NC. To build a robust model, we propose a novel loss function, called InfoLoss, that considers the reduction in entropy by observing each sequence of sentences to ultimately enhance the classification accuracy. The results of our comprehensive model evaluation using the I-CONECT dataset show that our framework can distinguish between MCI and NC with an average area under the curve of 84.75%.

White matter hyperintensities and the surrounding normal appearing white matter are associated with water channel disruption in the oldest old.

INTRODUCTION: Age-related magnetic resonance imaging (MRI) T2 white matter hyperintensities (WMHs) are common and associated with neurological decline. We investigated the histopathological underpinnings of MRI WMH and surrounding normal appearing white matter (NAWM), with a focus on astroglial phenotypes. METHODS: Brain samples from 51 oldest old Oregon Alzheimer's Disease Research Center participants who came to autopsy underwent post mortem (PM) 7 tesla MRI with targeted histopathological sampling of WMHs and NAWM. Stained slides were digitized and quantified. Mixed-effects models determined differences in molecular characteristics between WMHs and the NAWM and across NAWM. RESULTS: PM MRI-targeted WMHs are characterized by demyelination, microglial activation, and prominent astrocytic alterations, including disrupted aquaporin (AQP) expression. Similar changes occur within the surrounding NAWM in a pattern of decreasing severity with increased distance from WMHs. DISCUSSION: Decreased AQP expression within WMH and proximal NAWM suggest an overwhelmed system wherein water homeostasis is no longer maintained, contributing to WM damage in older individuals. HIGHLIGHTS: Post mortem magnetic resonance imaging (MRI) was used to characterize the pathology of white matter hyperintensities (WMHs) and surrounding normal appearing white matter (NAWM). Stained immunohistochemical (IHC) slides from targeted WMH and NAWM samples were digitized and quantified. WMHs and NAWM were associated with inflammation, demyelination, and gliosis. WMHs and NAWM astrocytic changes included decreased AQP1 and AQP4 expression. Abnormal NAWM pathology diminished in severity with increasing distance from WMH.

Differences in healthcare utilization between enrollees of fully integrated dual eligible special needs plans versus non-fully integrated plans.

BACKGROUND: Policymakers advocate care integration models to enhance Medicare and Medicaid service coordination for dually eligible individuals. One rapidly expanding model is the fully integrated dual eligible (FIDE) plan, a sub-type of the dual eligible special needs plan (D-SNP) in which a parent insurer manages Medicare and Medicaid spending for dually eligible individuals. We examined healthcare utilization differences among dually eligible individuals aged 65 years or older enrolled in D-SNPs by plan type (FIDE vs non-FIDE). METHODS: Using 2018 Medicare Advantage encounters and Medicaid claims of FIDE and non-FIDE enrollees in six states (AZ, CA, FL, NY, TN, WI), we compared healthcare utilization between plan types, adjusting for enrollee characteristics and county indicators. We applied propensity score weighting to address differences between FIDE and non-FIDE plan enrollees. RESULTS: In our main analysis, which included all dually eligible individuals in our sample, we observed no significant difference in healthcare utilization between FIDE and non-FIDE plan enrollees. However, we identified some differences in healthcare utilization between FIDE and non-FIDE plan enrollees in subgroup analyses. For example, among home and community-based service (HCBS) users, FIDE plan enrollees had 6.0 fewer hospitalizations per 1000 person-months (95% CI: -7.9, -4.0) and were 7.0 percentage points more likely to be discharged to home (95% CI: 2.6, 11.5) after hospitalization, compared to non-FIDE plan enrollees. CONCLUSION: While we found no differences in healthcare utilization between FIDE and non-FIDE plan enrollees when considering all dually eligible individuals in our sample, some differences emerged when focusing on subgroups. For example, HCBS users with FIDE plans had fewer hospitalizations and were more likely to be discharged to their home following hospitalization, compared to HCBS users with non-FIDE plans. These findings suggest that FIDE plans may improve care coordination for specific subsets of dually eligible individuals.

Salience Network Functional Connectivity Mediates Association Between Social Engagement and Cognition in Non-Demented Older Adults: Exploratory Investigation.

Background: Social engagement has beneficial effects during cognitive aging. Large-scale cognitive brain network functions are implicated in both social behaviors and cognition. Objective: We evaluated associations between functional connectivity (FC) of large-scale brain cognitive networks and social engagement, characterized by self-reported social network size and contact frequency. We subsequently tested large-scale brain network FC as a potential mediator of the beneficial relationship between social engagement and cognitive performance. Methods: 112 older adults (70.7±7.3 years, range 54.6-89.7; 84 women) completed the Lubben Social Network Scale 6 (LSNS-6), National Alzheimer's Coordinating Center (NACC) Uniform Data Set 3 (UDS-3) cognitive battery, and resting state fMRI. We completed seed-based correlational analysis in the default mode and salience networks. Significant associations between social engagement scores and cognitive performance, as well as between social engagement and FC of brain networks, informed the construction of mediation models. Results: Social engagement was significantly associated with executive function and global cognition, with greater social engagement associated with better cognitive performance. Social engagement was significantly associated with salience network FC, with greater social engagement associated with higher connectivity. Salience network FC partially mediated associations between social engagement and both executive function and global cognition. Conclusions: Our results suggest that the salience network is a key mediator of the beneficial relationship between social engagement and cognition in older adults.

A 10-week intergenerational program bringing together community-living older adults and preschool children (INTERACTION): a pilot feasibility non-randomised clinical trial.

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.

Cerebrospinal fluid biomarker profiling of diverse pathophysiological domains in Alzheimer's disease.

INTRODUCTION: While Alzheimer's disease (AD) is defined by amyloid-ß plaques and tau tangles in the brain, it is evident that many other pathophysiological processes such as inflammation, neurovascular dysfunction, oxidative stress, and metabolic derangements also contribute to the disease process and that varying contributions of these pathways may reflect the heterogeneity of AD. Here, we used a previously validated panel of cerebrospinal fluid (CSF) biomarkers to explore the degree to which different pathophysiological domains are dysregulated in AD and how they relate to each other. METHODS: Twenty-five CSF biomarkers were analyzed in individuals with a clinical diagnosis of AD verified by positive CSF AD biomarkers (AD, n = 54) and cognitively unimpaired controls negative for CSF AD biomarkers (CU-N, n = 26) using commercial single- and multi-plex immunoassays. RESULTS: We noted that while AD was associated with increased levels of only three biomarkers (MMP-10, FABP3, and 8OHdG) on a group level, half of all AD participants had increased levels of biomarkers belonging to at least two pathophysiological domains reflecting the diversity in AD. LASSO modeling showed that a panel of FABP3, 24OHC, MMP-10, MMP-2, and 8OHdG constituted the most relevant and minimally correlated set of variables differentiating AD from CU-N. Interestingly, factor analysis showed that two markers of metabolism and oxidative stress (24OHC and 8OHdG) contributed independent information separate from MMP-10 and FABP3 suggestive of two independent pathophysiological pathways in AD, one reflecting neurodegeneration and vascular pathology, and the other associated with metabolism and oxidative stress. DISCUSSION: Better understanding of the heterogeneity among individuals with AD and the different contributions of pathophysiological processes besides amyloid-ß and tau will be crucial for optimizing personalized treatment strategies. Highlights: A panel of 25 highly validated biomarker assays were measured in CSF.MMP10, FABP3, and 8OHdG were increased in AD in univariate analysis.Many individuals with AD had increased levels of more than one biomarker.Markers of metabolism and oxidative stress contributed to an AD multianalyte profile.Assessing multiple biomarker domains is important to understand disease heterogeneity.

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics.

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

Internet-Based Conversational Engagement Randomized Controlled Clinical Trial (I-CONECT) Among Socially Isolated Adults 75+ Years Old With Normal Cognition or Mild Cognitive Impairment: Topline Results.

BACKGROUND AND OBJECTIVES: Social isolation is a risk factor for cognitive decline and dementia. We conducted a randomized controlled clinical trial (RCT) of enhanced social interactions, hypothesizing that conversational interactions can stimulate brain functions among socially isolated older adults without dementia. We report topline results of this multisite RCT (Internet-based conversational engagement clinical trial [I-CONECT]; NCT02871921). RESEARCH DESIGN AND METHODS: The experimental group received cognitively stimulating semistructured conversations with trained interviewers via internet/webcam 4 times per week for 6 months (induction) and twice per week for an additional 6 months (maintenance). The experimental and control groups both received weekly 10 minutes telephone check-ins. Protocol modifications were required due to the coronavirus disease 2019 pandemic. RESULTS: A total of 186 participants were randomized. After the induction period, the experimental group had higher global cognitive test scores (Montreal Cognitive Assessment [primary outcome]; 1.75 points [p = .03]) compared with the control group. After induction, experimental group participants with normal cognition had higher language-based executive function (semantic fluency test [secondary outcome]; 2.56 points [p = .03]). At the end of the maintenance period, the experimental group of mild cognitive impairment subjects had higher encoding function (Craft Story immediate recall test [secondary outcome]; 2.19 points [p = .04]). Measure of emotional well-being improved in both control and experimental groups. Resting-state functional magnetic resonance imaging showed that the experimental group had increased connectivity within the dorsal attention network relative to the control group (p = .02), but the sample size was limited. DISCUSSION AND IMPLICATIONS: Providing frequent stimulating conversational interactions via the internet could be an effective home-based dementia risk-reduction strategy against social isolation and cognitive decline. CLINICAL TRIALS REGISTRATION NUMBER: NCT02871921.

Subject Harmonization of Digital Biomarkers: Improved Detection of Mild Cognitive Impairment from Language Markers.

Mild cognitive impairment (MCI) represents the early stage of dementia including Alzheimer's disease (AD) and is a crucial stage for therapeutic interventions and treatment. Early detection of MCI offers opportunities for early intervention and significantly benefits cohort enrichment for clinical trials. Imaging and in vivo markers in plasma and cerebrospinal fluid biomarkers have high detection performance, yet their prohibitive costs and intrusiveness demand more affordable and accessible alternatives. The recent advances in digital biomarkers, especially language markers, have shown great potential, where variables informative to MCI are derived from linguistic and/or speech and later used for predictive modeling. A major challenge in modeling language markers comes from the variability of how each person speaks. As the cohort size for language studies is usually small due to extensive data collection efforts, the variability among persons makes language markers hard to generalize to unseen subjects. In this paper, we propose a novel subject harmonization tool to address the issue of distributional differences in language markers across subjects, thus enhancing the generalization performance of machine learning models. Our empirical results show that machine learning models built on our harmonized features have improved prediction performance on unseen data. The source code and experiment scripts are available at https://github.com/illidanlab/subject_harmonization.

Including Socially Isolated Black, Older Old Adults (Aged 80 and Above) with and without Mild Cognitive Impairment in a Clinical Trial: Recruitment Strategies and Perspectives.

Objective: This study describes strategies for the recruitment of socially isolated older old Black individuals to participate in the "Internet-based conversational engagement clinical trial (I-CONECT)" (Clinical Trial.gov: NCT02871921) and lessons learned in this critical population segment. Methods: Best practice strategies to recruit the target population included mass mailings, advertisements, and direct community outreach, including the collaboration with a community group created to reach Black individuals interested in research participation. We also made protocol changes to measure recruitment criteria for older old Black adults more accurately and to increase their participation. Results: Descriptive data related to the challenges and successes in recruiting Black participants compared to the White participants is presented. The primary site contacted 17,523 primarily White potential participants and enrolled/randomized 145 White and 2 Asian/mixed race participants (0.8%). The Midwest site contacted 12,141 Black potential participants and enrolled/randomized 39 (0.3%) participants. Discussion: While best practices were employed, several factors complicated recruitment, including the need to adjust recruitment criteria, navigate regional regulations, and respect diverse community preferences. Conclusion: Older old African Americans are reachable and willing to participate in research when considering their beliefs and practices, influenced by their community and experience.

Differences in Life Space Activity Patterns Between Older Adults With Mild Cognitive Impairment Living Alone or as a Couple: Cohort Study Using Passive Activity Sensing.

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.

Feasibility of common, enjoyable game play for assessing daily cognitive functioning in older adults.

Background: Frequent digital monitoring of cognition is a promising approach for assessing endpoints in prevention and treatment trials of Alzheimer's disease and related dementias (ADRD). This study evaluated the feasibility of the MIND GamePack© for recurrent semi-passive assessment of cognition across a longitudinal interval. Methods: The MIND GamePack consists of four iPad-based games selected to be both familiar and enjoyable: Word Scramble, Block Drop, FreeCell, and Memory Match. Participants were asked to play 20 min/day for 5 days (100 min) for 4 months. Feasibility of use by older adults was assessed by measuring gameplay time and game performance. We also evaluated compliance through semi-structured surveys. A linear generalized estimating equation (GEE) model was used to analyze changes in gameplay time, and a regression tree model was employed to estimate the days it took for game performance to plateau. Subjective and environmental factors associated with gameplay time and performance were examined, including daily self-reported questions of memory and thinking ability, mood, sleep, energy, current location, and distractions prior to gameplay. Results: Twenty-six cognitively-unimpaired older adults participated (mean age ± SD = 71.9 ± 8.6; 73% female). Gameplay time remained stable throughout the 4-months, with an average compliance rate of 91% ± 11% (1946 days of data across all participants) and weekly average playtime of 210 ± 132 min per participant. We observed an initial learning curve of improving game performance which on average, plateaued after 22-39 days, depending on the game. Higher levels of self-reported memory and thinking ability were associated with more gameplay time and sessions. Conclusion: MIND GamePack is a feasible and well-designed semi-passive cognitive assessment platform which may provide complementary data to traditional neuropsychological testing in research on aging and dementia.

State-Level Adverse Outcomes Among Long-Term Services and Supports Users With Alzheimer's Disease and Related Dementias.

Home- and community-based services (HCBS) users, on average, experience hospitalizations more frequently than nursing facility residents. However, little is known about state-level variation in such adverse events among these groups. Using 2018 Medicare and Medicaid claims for dual-eligible beneficiaries with Alzheimer's disease and related dementias, we described hospitalization and emergency department (ED) visit rates among HCBS users and nursing facility residents and observed substantial state-level variation. In addition, consistent with prior evidence, we found more frequent hospitalizations and ED visits among HCBS users than nursing facility residents. The magnitude of this difference varied considerably across states, and the degree of variation was greatest among beneficiaries with six or more comorbid conditions. Our findings represent a crucial initial exploration of the state-level variation in adverse events among HCBS users and nursing facility residents, paving the way for further investigations into factors that contribute to this variability.

Mild Cognitive Impairment Subtype Performance in Comparison to Healthy Older Controls on the NIH Toolbox and Cogstate.

BACKGROUND: Early detection is necessary for the treatment of dementia. Computerized testing has become more widely used in clinical trials; however, it is unclear how sensitive these measures are to early signs of neurodegeneration. We investigated the use of the NIH Toolbox-Cognition (NIHTB-CB) and Cogstate-Brief computerized neuropsychological batteries in the identification of mild cognitive impairment (MCI) versus healthy older adults [healthy control (HC)] and amnestic (aMCI) versus nonamnestic MCI (naMCI). Exploratory analyses include investigating potential racial differences. METHODS: Two hundred six older adults were diagnosed as aMCI (n = 58), naMCI (n = 15), or cognitively healthy (HC; n = 133). RESULTS: The NIH Toolbox-CB subtests of Flanker, Picture Sequence Memory, and Picture Vocabulary significantly differentiated MCI from HC. Further, subtests from both computerized batteries differentiated patients with aMCI from those with naMCI. Although the main effect of race differences was noted on tests and in diagnostic groups was significant, there were no significant race-by-test interactions. CONCLUSIONS: Computer-based subtests vary in their ability to help distinguish MCI subtypes, though these tests provide less expensive and easier-to-administer clinical screeners to help identify patients early who may qualify for more comprehensive evaluations. Further work is needed, however, to refine computerized tests to achieve better precision in distinguishing impairment subtypes.

The Paradox in Positive and Negative Aspects of Emotional Functioning Among Older Adults with Early Stages of Cognitive Impairment.

Introduction: Emotional functioning in older adults is influenced by normal aging and cognitive impairment, likely heterogeneous across positive versus negative aspects of emotional functioning. Little is known about positive emotional experiences at the early stages of cognitive impairment. Methods: We assessed different aspects of emotional functioning among 448 participants aged 65+ (Normal Control (NC) = 276, Mild Cognitive Impairment (MCI) = 103, and mild dementia of the Alzheimer type (mild DAT) = 69) and tested moderators. Results: Compared to NC, older adults with MCI and mild DAT have maintained many positive aspects of emotional functioning, despite higher levels of negative affect, sadness, and loneliness. Among the oldest-old, the mild DAT group experienced higher fear and lower self-efficacy. Discussion: Older adults at early stages of cognitive impairment can experience positive aspects of emotional functioning, such as positive affect, purpose, and life satisfaction, all of which are important buildable psychological resources for coping.

Using Continuous Passive Assessment Technology to Describe Health and Behavior Patterns Preceding and Following a Cancer Diagnosis in Older Adults: Proof-of-Concept Case Series Study.

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.

Social Isolation and Loneliness in a Population Study of Cognitive Impairment: The MYHAT Study.

In this study, we examined associations of social isolation and loneliness with cognitive impairment among older adults from a Rust Belt region in Southwest Pennsylvania. We used data from the population-based Monongahela-Youghiogheny Healthy Aging Team (MYHAT) study. We found that (a) 11 items combined into two reliable composites of social isolation and loneliness; (b) unique to this study, providing unpaid help to others was an indicator of reduced social isolation; (c) social isolation and loneliness were positively associated with cognitive impairment; and (d) these associations were appreciably attenuated by general health and physical functional status and depressive symptoms, respectively. We concluded that social isolation and loneliness are differentially associated with older adults' cognitive health, and that their effects might operate through separate pathways. Approaches to address social isolation and loneliness should consider the community context and its implications for older adults' cognitive health.

Correlating continuously captured home-based digital biomarkers of daily function with postmortem neurodegenerative neuropathology.

BACKGROUND: Outcome measures available for use in Alzheimer's disease (AD) clinical trials are limited in ability to detect gradual changes. Measures of everyday function and cognition assessed unobtrusively at home using embedded sensing and computing generated "digital biomarkers" (DBs) have been shown to be ecologically valid and to improve efficiency of clinical trials. However, DBs have not been assessed for their relationship to AD neuropathology. OBJECTIVES: The goal of the current study is to perform an exploratory examination of possible associations between DBs and AD neuropathology in an initially cognitively intact community-based cohort. METHODS: Participants included in this study were ≥65 years of age, living independently, of average health for age, and followed until death. Algorithms, run on the continuously-collected passive sensor data, generated daily metrics for each DB: cognitive function, mobility, socialization, and sleep. Fixed postmortem brains were evaluated for neurofibrillary tangles (NFTs) and neuritic plaque (NP) pathology and staged by Braak and CERAD systems in the context of the "ABC" assessment of AD-associated changes. RESULTS: The analysis included a total of 41 participants (M±SD age at death = 92.2±5.1 years). The four DBs showed consistent patterns relative to both Braak stage and NP score severity. Greater NP severity was correlated with the DB composite and reduced walking speed. Braak stage was associated with reduced computer use time and increased total time in bed. DISCUSSION: This study provides the first data showing correlations between DBs and neuropathological markers in an aging cohort. The findings suggest continuous, home-based DBs may hold potential to serve as behavioral proxies that index neurodegenerative processes.

Demographically-adjusted normative data among Latinos for the version 3 of the Alzheimer's Disease Centers' Neuropsychological Test Battery in the Uniform Data Set.

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.