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.

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.

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

INTRODUCTION

While Alzheimer's disease (AD) is defined by amyloid-β 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-β 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.

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.

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.

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.

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.

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.

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.

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.

Short: Causal structural learning of conversational engagement for socially isolated older adults

Social isolation has become a growing public health concern in older adults and older adults with mild cognitive impairment. Coping strategies must be developed to increase social contact for socially isolated older adults. In this paper, we explored the conversational strategy between trained conversation moderators and socially isolated adults during a conversational engagement clinical trial (Clinicaltrials.gov: NCT02871921). We carried out structural learning and causality analysis to investigate the conversation strategies used by the trained moderators to engage socially isolated adults in the conversation and the causal effects of the strategy on engagement. Causal relations and effects were inferred between participants' emotions, the dialogue strategies used by moderators, and participants' following emotions. The results found in this paper may be used to support the development of cost-efficient, trustworthy AI- and/or robot-based platform to promote conversational engagement for older adults to address the challenges in social interaction.

Weak Social Networks in Late Life Predict Incident Alzheimer's Disease: The Kuakini Honolulu-Asia Aging Study

BACKGROUND

We assessed 10-year longitudinal associations between late-life social networks and incidence of all-cause dementia (ACD), Alzheimer's disease (AD), and vascular dementia (VaD) in Japanese-American men.

METHODS

We prospectively analyzed, from baseline (1991-1993) through 1999-2000, 2636 initially nondemented Kuakini Honolulu-Asia Aging Study participants who remained dementia-free during the first 3 years of follow-up. Global cognition was evaluated by the Cognitive Abilities Screening Instrument (CASI); depressive symptoms by the 11-item Center for Epidemiologic Studies Depression (CES-D) Scale; and social networks by the Lubben Social Network Scale (LSNS). Median split of LSNS scores defined weak/strong social network groups. A panel of neurologists and geriatricians diagnosed and classified dementia; AD and VaD diagnoses comprised cases in which AD or VaD, respectively, were considered the primary cause of dementia.

RESULTS

Median (range) baseline age was 77 (71-93) years. Participants with weak (LSNS score ≤29) versus strong (>29) social networks had higher age-adjusted incidence (in person-years) of ACD (12.6 vs. 8.7; p = .014) and AD (6.7 vs. 4.0; p = .007) but not VaD (2.4 vs. 1.4; p = .15). Kaplan-Meier curves showed a lower likelihood of survival free of ACD (log-rank p < .0001) and AD (p = .0006) for men with weak networks. In Cox proportional hazards models adjusting for age, education, APOE ɛ4, prevalent stroke, depressive symptoms, and CASI score (all at baseline), weak networks predicted increased incidence of ACD (hazard ratio [HR] = 1.52, p = .009) and AD (HR = 1.67, p = .014) but not VaD (p > .2).

CONCLUSION

Weak social networks may heighten the risk of dementia and AD, underscoring the need to promote social connectedness in older adults.

Detection of Mild Cognitive Impairment from Language Markers with Crossmodal Augmentation

Mild cognitive impairment is the prodromal stage of Alzheimer's disease. Its detection has been a critical task for establishing cohort studies and developing therapeutic interventions for Alzheimer's. Various types of markers have been developed for detection. For example, imaging markers from neuroimaging have shown great sensitivity, while its cost is still prohibitive for large-scale screening of early dementia. Recent advances from digital biomarkers, such as language markers, have provided an accessible and affordable alternative. While imaging markers give anatomical descriptions of the brain, language markers capture the behavior characteristics of early dementia subjects. Such differences suggest the benefits of auxiliary information from the imaging modality to improve the predictive power of unimodal predictive models based on language markers alone. However, one significant barrier to the joint analysis is that in typical cohorts, there are only very limited subjects that have both imaging and language modalities. To tackle this challenge, in this paper, we develop a novel crossmodal augmentation tool, which leverages auxiliary imaging information to improve the feature space of language markers so that a subject with only language markers can benefit from imaging information through the augmentation. Our experimental results show that the multi-modal predictive model trained with language markers and auxiliary imaging information significantly outperforms unimodal predictive models.

Predicting Progression from Normal to MCI and from MCI to AD Using Clinical Variables in the National Alzheimer's Coordinating Center Uniform Data Set Version 3: Application of Machine Learning Models and a Probability Calculator

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.

Postmortem 7T MRI for guided histopathology and evaluation of cerebrovascular disease

Postmortem (PM) magnetic resonance imaging (MRI) can serve as a bridge between in vivo imaging and histology by connecting MRI observed macrostructural findings to histological staining and microstructural changes. Data were acquired from 20 formalin-fixed brains including T2, T1, PD, and T2*-weighted images of left hemispheres and 6-mm-thick coronal slices. Tissue slices were bisected, aligned to MR images and used to guide histological sampling. Markers of myelin and oligodendroglia alterations were semiquantitatively rated and compared within white matter hyperintensities (WMHs) and normal-appearing white matter. Tissue priors were created from 3T in vivo data and used to guide segmentation of WMH. PM WMH and hemisphere volumes were compared to volumes derived from in vivo data. PM T2 WMH and T1 hemisphere volumes were correlated with in vivo 3T FLAIR WMH and T1 hemisphere volumes. WMH showed significant myelin loss, decreased GFAP expression and increased vimentin expression. MR-visible perivascular spaces and cortical microvascular lesions were successfully captured on histopathological sections. PM MRI can quantify cerebrovascular disease burden and guide tissue sampling, allowing for more comprehensive characterization of cerebrovascular disease that may be used to study etiologies of age-related cognitive change.

Emotional characteristics of socially isolated older adults with MCI using tablet administered NIH toolbox: I-CONECT study

Introduction

Examining the emotional functioning of individuals with mild cognitive impairment (MCI) could help describe their cognitive status and inform the development of interventions. This study compared the emotional characteristics of socially isolated older adults with and without MCI.

Methods

We used baseline data from the Internet-based Conversational Engagement Clinical Trial. Emotional characteristics were assessed with the National Institutes of Health Toolbox Emotion Battery (NIHTB-EB). MCI status was determined with a consensus clinical diagnosis.

Results

This study included 163 participants (mean age = 81.2 years, non-Hispanic Black = 20.7%, MCI = 52.8%). MCI was associated with higher negative affect and lower psychological well-being. Non-Hispanic Black participants scored lower in sadness, higher in positive affect, and higher in meaning and purpose than non-Hispanic White participants.

Conclusion

Older adults with MCI experience more negative emotions and worse psychological well-being than those with normal cognition. The NIHTB-EB appears to be a sensitive tool to detect emotional characteristics associated with cognitive decline.

Unobtrusive Sensing Technology Detects Ecologically Valid Spatiotemporal Patterns of Daily Routines Distinctive to Persons With Mild Cognitive Impairment

BACKGROUND

The ability to capture people's movement throughout their home is a powerful approach to inform spatiotemporal patterns of routines associated with cognitive impairment. The study estimated indoor room activities over 24 hours and investigated relationships between diurnal activity patterns and mild cognitive impairment (MCI).

METHODS

One hundred and sixty-one older adults (26 with MCI) living alone (age = 78.9 ± 9.2) were included from 2 study cohorts-the Oregon Center for Aging & Technology and the Minority Aging Research Study. Indoor room activities were measured by the number of trips made to rooms (bathroom, bedroom, kitchen, living room). Trips made to rooms (transitions) were detected using passive infrared motion sensors fixed on the walls for a month. Latent trajectory models were used to identify distinct diurnal patterns of room activities and characteristics associated with each trajectory.

RESULTS

Latent trajectory models identified 2 diurnal patterns of bathroom usage (high and low usage). Participants with MCI were more likely to be in the high bathroom usage group that exhibited more trips to the bathroom than the low-usage group (odds ratio [OR] = 4.1, 95% CI [1.3-13.5], p = .02). For kitchen activity, 2 diurnal patterns were identified (high and low activity). Participants with MCI were more likely to be in the high kitchen activity group that exhibited more transitions to the kitchen throughout the day and night than the low kitchen activity group (OR = 3.2, 95% CI [1.1-9.1], p = .03).

CONCLUSIONS

The linkage between bathroom and kitchen activities with MCI may be the result of biological, health, and environmental factors in play. In-home, real-time unobtrusive-sensing offers a novel way of delineating cognitive health with chronologically-ordered movement across indoor locations.

Depressive symptoms in older adult couples: Associations with dyadic physical health, social engagement, and close friends

Objective

The objective of this study was to examine associations between level of depressive symptoms in older adult spouse/partner couples and their physical health and social factors (social activity and number of close friends).

Methods

Using data from 116 community-dwelling couples (age 76.2 ± 8.5), we simultaneously analyzed associations between depressive symptoms (Geriatric Depression Scale, range 0-11) and dyadic physical health, engagement in social activities, and connectedness with close friends.

Results

Greater engagement in social activities was associated with fewer depressive symptoms in men, whereas more close friendships were associated with fewer depressive symptoms in women, controlling for partner effects, age, education, and cognitive function, with good model fit. Additionally, more disparate physical health within the couple (latent incongruence score) was associated with greater depressive symptoms in men.

Discussion

Less social activity and fewer close friends were associated with depressive symptoms in older adult couples, but may be distinctly influential depending on gender and in the context of the older adult couple's physical health.

ARMADA: Assessing reliable measurement in Alzheimer's disease and cognitive aging project methods

INTRODUCTION

Early detection of cognitive decline in older adults is a public health priority. Advancing Reliable Measurement in Alzheimer's Disease and Cognitive Aging (ARMADA), a multisite study, is validating cognition, emotion, motor, and sensory modules of the National Institutes of Health Toolbox for Assessment of Neurological and Behavioral Function (NIHTB) in the aging spectrum from cognitively normal to dementia of the Alzheimer's type (DAT).

METHODS

Participants 65 to 85 years old, in demographic groups racially proportional to the general US population, are recruited in one of three groups to validate the NIHTB: cognitively normal, amnestic mild cognitive impairment (aMCI), or mild DAT. Additional special emphasis cohorts include (1) Blacks in the three clinical groups; (2) Spanish-speakers in the three clinical groups; (3) cognitively normal, population-proportional, over age 85.

DISCUSSION

Longitudinal study will determine whether NIHTB can predict cognitive decline and is associated with Alzheimer's disease biomarkers. Here, we detail the methods for the ARMADA study.

Committee on High-quality Alzheimer's Disease Studies (CHADS) consensus report

BACKGROUND

Consensus guidance for the development and identification of high-quality Alzheimer's disease clinical trials is needed for protocol development and conduct of clinical trials.

METHODS

An ad hoc consensus committee was convened in conjunction with the Alzheimer's Association to develop consensus recommendations.

RESULTS

Consensus was readily reached for the need to provide scientific justification, registration of trials, institutional review board oversight, conflict of interest disclosure, funding source disclosure, defined trial population, recruitment resources, definition of the intervention, specification of trial duration, appropriate payment for participant engagement, risk-benefit disclosure as part of the consent process, and the requirement to disseminate and/or publish trial results even if the study is negative.

CONCLUSIONS

This consensus guidance should prove useful for the protocol development and conduct of clinical trials, and may further provide a platform for the development of education materials that may help guide appropriate clinical trial participation decisions for potential trial participants and the general public.

OPTIMIZE WAV2VEC2S ARCHITECTURE FOR SMALL TRAINING SET THROUGH ANALYZING ITS PRE-TRAINED MODELS ATTENTION PATTERN

Transformer-based automatic speech recognition (ASR) systems have shown their success in the presence of large datasets. But, in medical research, we have to create ASR for the non-typical population, i.e. pre-school children with speech disorders, with small training dataset. To increase training efficiency on small datasets, we optimize the architecture of Wav2Vec 2.0, a variation of Transformer, through analyzing its pre-trained model's block-level attention pattern. We show that block-level patterns can serve as an indicator for narrowing down the optimization direction. To ensure the reproducibility of our experiments, we leverage Librispeech-100-clean as training data to simulate the limited data condition. We leverage two techniques, local attention mechanism and cross-block parameter sharing, with counter-intuitive configurations. Our optimized architecture outperforms the vanilla architecture about 1.8% absolute word error rate (WER) on dev-clean and 1.4% on test-clean.

Can changes in social contact (frequency and mode) mitigate low mood before and during the COVID-19 pandemic? The I-CONECT project

BACKGROUND/OBJECTIVES

The coronavirus disease 2019 (COVID-19) global outbreak allowed a natural experiment to observe how older adults changed social patterns and how it affected their emotional well-being. We studied the frequency and modes of social contact and their effects on older adults' mood before and during the COVID-19 pandemic.

DESIGN

Phone-based surveys were administered weekly before and during the COVID-19 pandemic.

SETTING

Participants were recruited from Portland, Oregon, and Detroit, Michigan.

PARTICIPANTS

Older adults ≥75 years old (n = 155, age = 81.0 ± 4.5, 72.3% women) were included in a randomized controlled trial, the Internet-Based Conversational Engagement Clinical Trial (I-CONECT).

MEASUREMENTS

Low mood was self-reported as feeling downhearted or blue for three or more days in the past week. Social contact was self-reported by the amount of time spent in interactions, with whom (family, friends, others), and via which modes (in-person, phone/video call, text/email/letter).

RESULTS

A total of 5525 weeks of data were derived from 155 participants. Before the COVID-19 pandemic, average social interaction time spent in-person, on phone/video call, and via text/email/letter was 406, 141, and 68 min/week, respectively. During the COVID-19 pandemic, time spent in-person was reduced by 135 min/week, while time spent via phone/video call and writing increased by 33 and 26 mins/week, respectively. In-person family contact was associated with less low mood regardless of the pandemic (odds ratio = 0.92, p < 0.05). There was a COVID-19 × text/email/letter with friends interaction (odds ratio = 0.77, p = 0.03), suggesting that during the COVID-19 pandemic, an increase of 1 h of writing with friends per week was associated with a 23% decrease in the likelihood of experiencing low mood.

CONCLUSION

The lost in-person time relating to COVID-19 restrictions tended to be partially compensated for with increased calls and writing time, although overall social interaction time decreased. During the COVID-19 pandemic, at least two types of social interactions (writing to friends and in-person family time) showed promise for mitigating low mood for older adults with limited social resources.

The epidemiology is promising, but the trial evidence is weak. Why pharmacological dementia risk reduction trials haven't lived up to expectations, and where do we go from here?

There is an urgent need for interventions that can prevent or delay cognitive decline and dementia. Decades of epidemiological research have identified potential pharmacological strategies for risk factor modification to prevent these serious conditions, but clinical trials have failed to confirm the potential efficacy for such interventions. Our multidisciplinary international group reviewed seven high-potential intervention strategies in an attempt to identify potential reasons for the mismatch between the observational and trial results. In considering our findings, we offer constructive recommendations for the next steps. Overall, we observed some differences in the observational evidence base for the seven strategies, but several common methodological themes that emerged. These themes included the appropriateness of trial populations and intervention strategies, including the timing of interventions and other aspects of trials methodology. To inform the design of future clinical trials, we provide recommendations for the next steps in finding strategies for effective dementia risk reduction.

Association Between Mild Cognitive Impairment and Seasonal Rest-Activity Patterns of Older Adults

Seasonal variation in rest-activity patterns has been observed in healthy adult populations. This study examined seasonal variation in total time spent overnight in the bedroom by cognitively intact older adults and older adults with mild cognitive impairment (MCI). We hypothesize that seasonal variation in rest-activity patterns is observed in the cognitively intact group and that this variation is disturbed in those with MCI. Study participants were 128 older adults; mean age 85.2 years. Ninety-eight were cognitively intact, and 30 had been diagnosed with MCI. All were enrolled in an ongoing longitudinal study using in-home passive monitoring technology. Infrared presence sensors were placed throughout each participant's home to monitor movement and presence in each room of the home. Activity data was collected from the sensors over a period of up to 527 days. Overnight time in bedroom was found to vary seasonally for the cognitively intact group, with longer times spent overnight in the bedroom during the winter months. This seasonal variation was not observed for those with non-amnestic MCI. MCI is associated with an attenuation of seasonal variation in total time spent in the bedroom at night. Detection of changes in infradian sleep patterns may be an early marker of cognitive decline. Which key determinants are driving these disturbed rhythms, such as features intrinsic to changes in the brain or to environmental factors or external cues, remains an important question for ongoing and future studies.

The Joint Effects of Acoustic and Linguistic Markers for Early Identification of Mild Cognitive Impairment

In recent years, behavioral markers such as spoken language and lexical preferences have been studied in the early detection of mild cognitive impairment (MCI) using conversations. While the combination of linguistic and acoustic signals have been shown to be effective in detecting MCI, they have generally been restricted to structured conversations in which the interviewee responds to fixed prompts. In this study, we show that linguistic and acoustic features can be combined synergistically to identify MCI in semi-structured conversations. Using conversational data from an on-going clinical trial (Clinicaltrials.gov: NCT02871921), we find that the combination of linguistic and acoustic features on semi-structured conversations achieves a mean AUC of 82.7, significantly (p < 0.01) out-performing linguistic-only (74.9 mean AUC) or acoustic-only (65.0 mean AUC) detections on hold-out data. Additionally, features (linguistic, acoustic and combination) obtained from semi-structured conversations outperform their counterparts obtained from structured weekly conversations in identifying MCI. Some linguistic categories are significantly better at predicting MCI status (e.g., death, home) than others.

Life activity patterns and mild cognitive impairment in older adults

BACKGROUND

Shifting and diminished circadian and activity rhythms characterize older adults with cognitive impairment, but studies rarely consider both temporal and spatial aspects of life activity patterns. We examined hourly in-home and out-of-home activity patterns using latent group trajectory models to characterize older adults with and without mild cognitive impairment (MCI).

METHOD

One hundred and seven older adults (19 with MCI) living alone (age=81.1±7.8, 76.6% women) from the Oregon Center for Aging & Technology (ORCATECH) were included. MCI was determined by the Clinical Dementia Rating (CDR) score = 0.5. In-home and out-of-home activities were collected via passive infrared motion sensors in each room and contact sensors on the doors to the home for one month. Daily life activity patterns were estimated based on: 1) the hourly average amount of time the participant spent in each major life space (bathroom; bedroom; kitchen; living room) and out-of-home, and 2) the hourly average number of trips the participant made to each room and out-of-home.

RESULT

Latent trajectory models identified two distinct patterns for three features: time spent out-of-home (high/low outdoor), time spent in the kitchen (high/low kitchen occupancy), and trips to the bathroom (more/fewer bathroom trips) from 5 am to 1 pm. Older adults with MCI were more likely to follow a pattern of low outdoor time (β=-2.65; p=0.03), high kitchen occupancy (β=1.68; p=0.01), and more bathroom trips (β=1.60; p=0.02) than those with intact cognition. There were no differences in the bedroom patterns or time spent in the bathroom in older adults with and without MCI from 5 am to 1 pm. Potential covariates were controlled in the models (age, gender, years of education, sleep duration, gait speed, and the number of rooms in the house).

CONCLUSION

Different social and mealtime activity patterns from early morning to early afternoon may signal cognitive changes and contraction of life-space. These may be caused by environmental, as well as chronobiological changes accompanying MCI. Unobtrusive measurement of in-home and out-of-home activity patterns may be useful for integrating with chronotherapies in developing strategies to mitigate potential daily mal-entrainment for older adults at risk for MCI and dementia.

The joint effects of audio and linguistic markers for early identification of mild cognitive impairment

BACKGROUND

In recent years, speech-related behavioral markers such as spoken language and lexical preferences have been studied in the early detection of mild cognitive impairment (MCI). While the combination of linguistic and acoustic signals has been shown to be effective in detecting MCI, they have generally been restricted to structured conversations in which the interviewee responds to fixed prompts. In this study, we propose a stratification scheme that proves to be effective in extracting quality linguistic and acoustic MCI markers in semi-structured more natural conversational settings.

METHOD

We obtained transcripts and audio recordings from an ongoing single blind randomized controlled clinical trial NCT02871921. Briefly this clinical trial aims to improve psychological well-being and cognitive functions of the non-demented (normal or MCI) older adults aged 75 and older by enhancing their social interactions. The experimental group receives 30 minutes of semi-structured conversations (video chats) with interviewers four times per week, for up to one year. Recruitment started in July 2018 and we used the data from the first 39 participants enrolled in the experimental group with audio and transcribed data as of March 2020 in the current analysis. Our stratification scheme eliminates drastic train-test distribution mismatch, allowing for apples-to-apples comparison across participant conversations, despite drastic variations in word choices, sentence structure and length of conversations - obstacles that typically present challenges to the semi-structured conversational setting for automatic acoustic marker extraction.

RESULTS

Figure 1 gives an overview of our MCI prediction pipeline. The combination of linguistic and audio features achieve a mean AUC of 82.7, significantly (p<0.01) out-performing linguistic-only (AUC = 74.9) or acoustic-only (AUC = 65.0) detections on hold-out data. Our subtopic stratification method identifies several conserved clusters (Figure 2) in interview structure, allowing more balanced train-test split in terms of linguistic properties, despite the semi-structured nature of conversations.

CONCLUSION

A combination of linguistic and audio features improves the ability to detect MCI beyond that achieved by each feature alone in semi-structured conversation. Our method provides a potentially valuable approach for using speech for early detection of MCI and clinical trials enrichment.

Reproducibility and replicability of high-frequency, in-home digital biomarkers in reducing sample sizes for clinical trials

INTRODUCTION

Reproducibility and replicability of results are rarely achieved for digital biomarkers analyses. We reproduced and replicated previously reported sample size estimates based on digital biomarker and neuropsychological test outcomes in a hypothetical 4-year early-phase Alzheimer's disease trial.

METHODS

Original data and newly collected data (using a different motion sensor) came from the Oregon Center for Aging & Technology (ORCATECH). Given trajectories of those with incident mild cognitive impairment and normal cognition would represent trajectories of the control and experimental groups in a hypothetical trial, sample sizes to provide 80% power to detect effect sizes ranging from 20% to 50% were calculated.

RESULTS

For the reproducibility, identical P-values and slope estimates were found with both digital biomarkers and neuropsychological test measures between the previous and current studies. As for the replicability, a greater correlation was found between original and replicated sample size estimates for digital biomarkers (r = 0.87, P < .001) than neuropsychological test outcomes (r = 0.75, P < .001).

DISCUSSION

Reproducibility and replicability of digital biomarker analyses are feasible and encouraged to establish the reliability of findings.

Dementia risk reduction: why haven't the pharmacological risk reduction trials worked? An in-depth exploration of seven established risk factors

Identifying the leading health and lifestyle factors for the risk of incident dementia and Alzheimer's disease has yet to translate to risk reduction. To understand why, we examined the discrepancies between observational and clinical trial evidence for seven modifiable risk factors: type 2 diabetes, dyslipidemia, hypertension, estrogens, inflammation, omega-3 fatty acids, and hyperhomocysteinemia. Sample heterogeneity and paucity of intervention details (dose, timing, formulation) were common themes. Epidemiological evidence is more mature for some interventions (eg, non-steroidal anti-inflammatory drugs [NSAIDs]) than others. Trial data are promising for anti-hypertensives and B vitamin supplementation. Taken together, these risk factors highlight a future need for more targeted sample selection in clinical trials, a better understanding of interventions, and deeper analysis of existing data.

Type and frequency of social contacts associated with low mood before and during the COVID-19 pandemic in older adults: I-CONECT project

BACKGROUND

Social isolation is a risk factor for dementia, but the underlying mechanism is not well understood. It is possible that lack of social contacts negatively affects emotional well-being, which leads to cognitive decline. To shed light on this potential mediation mechanism, we examined changes in type and frequency of social contacts and their effects on mood using data collected before and during the COVID-19 pandemic among socially isolated older adults aged 75 and older.

METHOD

The data come from an ongoing randomized controlled trial, the Internet-Based Conversational Engagement Clinical Trial (I-CONECT, ClinicalTirals.gov: NCT02871921). One hundred forty-six participants (age=81.0±4.5, 71.9% women) who were in the trial both before and during the pandemic and whose data were available as of November of 2020 were included in the current analysis. Weekly health questionnaires administered on all participants regardless of treatment assignments were collected before and during the COVID-19 pandemic. Low mood ("Blueness") was self-reported as feeling downhearted or blue for three or more days in the past week (YES/NO). Social contacts were self-reported by amount of time they had interacted, with whom (family; friends; others), and via which modalities (in-person; phone/video call; text/email).

RESULT

A total of 4,774 weeks of survey data were analyzed (3,047 before COVID 19). The weekly average time spent in-person, on phone/video call, and via text/email were 282, 113, and 44 minutes, respectively. During the COVID-19 pandemic, participants on average spent 82 minutes less in total social contact per week (in-person: reduced 123 minutes, video/call: increased 28 minutes, text/email: increased 13 minutes per week). Generalized estimating equation model revealed that in-person family contact was associated with less blueness regardless of the pandemic (OR=0.91, p=0.04). There was a COVID*text/email time with friends interaction (OR=0.68, p=0.03), suggesting that during the COVID-19 pandemic, an increase of 1 hour of texting/emailing with friends per week was associated with 32% decrease in experiencing blueness three or more days per week.

CONCLUSION

In-person family time is beneficial for mental health. While in-person contacts become less frequent during the COVID-19 pandemic, increased text/email time with friends becomes an alternative to maintain mental health for socially isolated older adults.

In-Home Mobility Frequency and Stability in Older Adults Living Alone With or Without MCI: Introduction of New Metrics

Background: Older adults spend a considerable amount of time inside their residences; however, most research investigates out-of-home mobility and its health correlates. We measured indoor mobility using room-to-room transitions, tested their psychometric properties, and correlated indoor mobility with cognitive and functional status. Materials and Methods: Community-dwelling older adults living alone (n = 139; age = 78.1 ± 8.6 years) from the Oregon Center for Aging & Technology (ORCATECH) and Minority Aging Research Study (MARS) were included in the study. Two indoor mobility features were developed using non-parametric parameters (frequency; stability): Indoor mobility frequency (room-to-room transitions/day) was detected using passive infrared (PIR) motion sensors fixed on the walls in four geographic locations (bathroom; bedroom; kitchen; living room) and using door contact sensors attached to the egress door in the entrance. Indoor mobility stability was estimated by variances of number of room-to-room transitions over a week. Test-retest reliability (Intra-class coefficient, ICC) and the minimal clinically important difference (MCID) defined as the standard error of measurement (SEM) were generated. Generalized estimating equations models related mobility features with mild cognitive impairment (MCI) and functional status (gait speed). Results: An average of 206 days (±127) of sensor data were analyzed per individual. Indoor mobility frequency and stability showed good to excellent test-retest reliability (ICCs = 0.91[0.88-0.94]; 0.59[0.48-0.70]). The MCIDs of mobility frequency and mobility stability were 18 and 0.09, respectively. On average, a higher indoor mobility frequency was associated with faster gait speed (β = 0.53, p = 0.04), suggesting an increase of 5.3 room-to-room transitions per day was associated with an increase of 10 cm/s gait speed. A decrease in mobility stability was associated with MCI (β = -0.04, p = 0.03). Discussion: Mobility frequency and stability in the home are clinically meaningful and reliable features. Pervasive-sensing systems deployed in homes can objectively reveal cognitive and functional status in older adults who live alone.

Older Adults' Daily Activity and Mood Changes Detected During the COVID-19 Pandemic Using Remote Unobtrusive Monitoring Technologies

Background and Objectives

The coronavirus disease 2019 (COVID-19) pandemic has limited older adults' access to in-person medical care, including screenings for cognitive and functional decline. Remote, technology-based tools have shown recent promise in assessing changes in older adults' daily activities and mood, which may serve as indicators of underlying health-related changes (e.g., cognitive decline). This study examined changes in older adults' driving, computer use, mood, and travel events prior to and following the COVID-19 emergency declaration using unobtrusive monitoring technologies and remote online surveys. As an exploratory aim, the impact of mild cognitive impairment (MCI) on these changes was assessed.

Research Design and Methods

Participants were 59 older adults (41 cognitively intact and 18 MCI) enrolled in a longitudinal aging study. Participants had their driving and computer use behaviors recorded over a 5-month period (75 days pre- and 76 days post-COVID emergency declaration) using unobtrusive technologies. Measures of mood, overnight guests, and frequency of overnight travel were also collected weekly via remote online survey.

Results

After adjusting for age, gender, and education, participants showed a significant decrease in daily driving distance, number of driving trips, highway driving, and nighttime driving, post-COVID-19 as compared to pre-COVID-19 (p < .001) based on generalized estimating equation models. Further, participants spent more time on the computer per day post-COVID-19 (p = .03). Participants endorsed increases in blue mood (p < .01) and loneliness (p < .001) and decreases in travel away from home and overnight visitors (p < .001) from pre- to post-COVID-19. Cognitive status did not impact these relationships.

Discussion and Implications

From pre- to post-COVID-19 emergency declaration, participants drove and traveled less, used their computer more, had fewer overnight visitors, and reported greater psychological distress. These results highlight the behavioral and psychological effects of stay-at-home orders on older adults who are cognitively intact and those with MCI.

The Internet-Based Conversational Engagement Clinical Trial (I-CONECT) in Socially Isolated Adults 75+ Years Old: Randomized Controlled Trial Protocol and COVID-19 Related Study Modifications

Background: Increasing social interactions through communication technologies could offer a cost-effective prevention approach that slows cognitive decline and delays the onset of Alzheimer's disease. This paper describes the protocol of an active project named "Internet-based conversational engagement clinical trial (I-CONECT)" (ClinicalTrials.gov: NCT02871921). The COVID-19 pandemic related protocol modifications are also addressed in the current paper. Methods: I-CONECT is a multi-site, assessor-blind, randomized controlled behavioral intervention trial (RCT). We aim to randomize 320 socially isolated adults 75+ years old [160 Caucasian and 160 African American participants, 50:50 split between those with normal cognition and mild cognitive impairment (MCI)] recruited from the community to either the video chat intervention group or the control group (1:1 allocation). Those in the video chat group receive a computer and Internet service for the duration of the study, which they use to video chat with study staff for 30 min/day 4×/week for 6 months (high dose), and then 2×/week for an additional 6 months (maintenance dose). Both video chat and control groups have a brief (about 10 min) telephone check-in with study staff once per week. The primary outcome is the change in global cognitive function measured by Montreal Cognitive Assessment (MoCA) from baseline to 6 months. Secondary outcomes include changes in cognition in memory and executive function domains, emotional well-being measured by NIH Toolbox emotional battery, and daily functional abilities assessed with the Revised Observed Tasks of Daily Living (OTDL-R). Eligible participants have MRIs at baseline and 6 months. Participants contribute saliva for genetic testing (optional consent), and all video chats, weekly check-in calls and neuropsychological assessment sessions are recorded for speech and language analysis. The pandemic halted research activities and resulted in protocol modifications, including replacing in-person assessment with remote assessment, remote deployment of study equipment, and revised targeted sample size. Discussion: This trial provides user-friendly hardware for the conversational-based intervention that can be easily provided at participants' homes. The trial aspires to use age and culture-specific conversational materials and a related platform developed in this trial for enhancing cognitive reserve and improving cognitive function.