Objective estimation of m-CTSIB balance test scores using wearable sensors and machine learning.

Accurate balance assessment is important in healthcare for identifying and managing conditions affecting stability and coordination. It plays a key role in preventing falls, understanding movement disorders, and designing appropriate therapeutic interventions across various age groups and medical conditions. However, traditional balance assessment methods often suffer from subjectivity, lack of comprehensive balance assessments and remote assessment capabilities, and reliance on specialized equipment and expert analysis. In response to these challenges, our study introduces an innovative approach for estimating scores on the Modified Clinical Test of Sensory Interaction on Balance (m-CTSIB). Utilizing wearable sensors and advanced machine learning algorithms, we offer an objective, accessible, and efficient method for balance assessment. We collected comprehensive movement data from 34 participants under four different sensory conditions using an array of inertial measurement unit (IMU) sensors coupled with a specialized system to evaluate ground truth m-CTSIB balance scores for our analysis. This data was then preprocessed, and an extensive array of features was extracted for analysis. To estimate the m-CTSIB scores, we applied Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBOOST algorithms. Our subject-wise Leave-One-Out and 5-Fold cross-validation analysis demonstrated high accuracy and a strong correlation with ground truth balance scores, validating the effectiveness and reliability of our approach. Key insights were gained regarding the significance of specific movements, feature selection, and sensor placement in balance estimation. Notably, the XGBOOST model, utilizing the lumbar sensor data, achieved outstanding results in both methods, with Leave-One-Out cross-validation showing a correlation of 0.96 and a Mean Absolute Error (MAE) of 0.23 and 5-fold cross-validation showing comparable results with a correlation of 0.92 and an MAE of 0.23, confirming the model's consistent performance. This finding underlines the potential of our method to revolutionize balance assessment practices, particularly in settings where traditional methods are impractical or inaccessible.

Social Determinants of Health, Risk and Resilience against Alzheimer's Disease and Related Dementias: The Healthy Brain Initiative.

Background: Few studies have investigated associations between perceived social determinants of health (SDOH) and Alzheimer's disease and related dementia (ADRD) biomarkers or between SDOH and resilience against ADRD. Objective: To examine associations between perceived and objective SDOH and ADRD-related outcomes. Methods: We used cross-sectional data on≥50-year-olds without dementia in the Healthy Brain Initiative (n = 162). Questionnaires captured trust in neighbors and indices of perceived neighborhood greenspace access, time spent in neighborhood greenspaces, and interpersonal discrimination. Residential addresses were linked to 2021 Area Deprivation Index scores. The Vulnerability Index (VI) is based on 12 dementia risk factors (e.g., age, race/ethnicity, diabetes) and Resilience Index (RI) is based on 6 protective factors (e.g., diet, mindfulness, physical activity). Cognitive measured included number symbol coding task and Montreal Cognitive Assessment. Biomarkers included Aß42/40 and pTau-217/npTau-217, hippocampal and white matter hyperintensity volume, lipoprotein A, and high-sensitivity c-reactive protein. Results: Perceived greater access to greenspaces (estimate = 2.83, 95% CI = 1.40-4.26) and greater time in neighborhood greenspaces were associated with greater RI scores (estimate = 2.30, 95% CI = 1.24-3.35). Reporting greater discrimination (estimate = 0.10, 95% CI = 0.04-0.16) and living in higher deprivation neighborhoods were associated with greater VI scores (estimate = 0.017, 95% CI = 0.003-0.032). Greater discrimination was associated with greater white matter hyperintensity volume (estimate = 0.27, 95% CI = 0.04-0.51). Conclusions: Perceived greenspace access and time spent in greenspaces were associated with resilience against ADRD, and interpersonal discrimination was associated with vulnerability to ADRD. Future work needs to validate perceived SDOH measures, examine associations in racially/ethnic diverse populations, and investigate longitudinal associations between SDOH and ADRD-related biomarkers.

The Mediating Role of Cortical Atrophy on the Relationship between the Resilience Index and Cognitive Function: Findings from the Healthy Brain Initiative.

Background: Lifestyle factors are linked to differences in brain aging and risk for Alzheimer's disease, underscored by concepts like 'cognitive reserve' and 'brain maintenance'. The Resilience Index (RI), a composite of 6 factors (cognitive reserve, physical and cognitive activities, social engagement, diet, and mindfulness) provides such a holistic measure. Objective: This study aims to examine the association of RI scores with cognitive function and assess the mediating role of cortical atrophy. Methods: Baseline data from 113 participants (aged 45+, 68% female) from the Healthy Brain Initiative were included. Life course resilience was estimated with the RI, cognitive performance with Cognivue®, and brain health using a machine learning derived Cortical Atrophy Score (CAS). Mediation analysis probed the relationship between RI, cognitive outcomes, and cortical atrophy. Results: In age and sex adjusted models, the RI was significantly associated with CAS (ß= -0.25, p = 0.006) and Cognivue® scores (ß= 0.32, p < 0.001). The RI-Cognivue® association was partially mediated by CAS (ß= 0.07; 95% CI [0.02, 0.14]). Conclusions: Findings revealed that the collective effect of early and late-life lifestyle resilience factors on cognition are partially explained by their association with less brain atrophy. These findings underscore the value of comprehensive lifestyle assessments in understanding the risk and progression of cognitive decline and Alzheimer's disease in an aging population.

The Healthy Brain Initiative (HBI): A prospective cohort study protocol.

BACKGROUND: The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging. METHODS: HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline. ETHICS AND EXPECTED IMPACT: HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, creating comprehensive diagnostic evaluations, and providing the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

The Healthy Brain Initiative (HBI): A prospective cohort study protocol.

Background: The Health Brain Initiative (HBI), established by University of Miami's Comprehensive Center for Brain Health (CCBH), follows racially/ethnically diverse older adults without dementia living in South Florida. With dementia prevention and brain health promotion as an overarching goal, HBI will advance scientific knowledge by developing novel assessments and non-invasive biomarkers of Alzheimer's disease and related dementias (ADRD), examining additive effects of sociodemographic, lifestyle, neurological and biobehavioral measures, and employing innovative, methodologically advanced modeling methods to characterize ADRD risk and resilience factors and transition of brain aging. Methods: HBI is a longitudinal, observational cohort study that will follow 500 deeply-phenotyped participants annually to collect, analyze, and store clinical, cognitive, behavioral, functional, genetic, and neuroimaging data and biospecimens. Participants are ≥50 years old; have no, subjective, or mild cognitive impairment; have a study partner; and are eligible to undergo magnetic resonance imaging (MRI). Recruitment is community-based including advertisements, word-of-mouth, community events, and physician referrals. At baseline, following informed consent, participants complete detailed web-based surveys (e.g., demographics, health history, risk and resilience factors), followed by two half-day visits which include neurological exams, cognitive and functional assessments, an overnight sleep study, and biospecimen collection. Structural and functional MRI is completed by all participants and a subset also consent to amyloid PET imaging. Annual follow-up visits repeat the same data and biospecimen collection as baseline, except that MRIs are conducted every other year after baseline. Ethics and expected impact: HBI has been approved by the University of Miami Miller School of Medicine Institutional Review Board. Participants provide informed consent at baseline and are re-consented as needed with protocol changes. Data collected by HBI will lead to breakthroughs in developing new diagnostics and therapeutics, create comprehensive diagnostic evaluations, and provide the evidence base for precision medicine approaches to dementia prevention with individualized treatment plans.

Emerging drugs for dementia with Lewy Bodies: a review of Phase II & III trials.

INTRODUCTION: Despite faster cognitive decline and greater negative impact on patients and family caregivers, drug development efforts in Dementia with Lewy Bodies (DLB) fall behind those for Alzheimer's Disease (AD). Current off-label drug DLB treatment options are limited to symptomatic agents developed to address cognitive deficits in AD, motor deficits in Parkinson's Disease, or behavioral symptoms in psychiatric disease. Aided by recent improvements in DLB diagnosis, a new focus on the development of disease-modifying agents (DMA) is emerging. AREAS COVERED: Driven by evidence supporting different pathological mechanisms in DLB and PDD, this review assesses the evidence on symptomatic drug treatments and describes current efforts in DMA development in DLB. Specifically, our goals were to: (1) review evidence supporting the use of symptomatic drug treatments in DLB; (2) review the current DMA pipeline in DLB with a focus on Phase II and III clinical trials; and (3) identify potential issues with the development of DMA in DLB. Included in this review were completed and ongoing drug clinical trials in DLB registered on ClinicalTrials.gov (no time limits set for the search) or disseminated at the 2023 international conference on Clinical Trials in AD. Drug clinical trials registered in non-US clinical trial registries were not included. EXPERT OPINION: Adoption of current symptomatic drug treatments used off-label in DLB relied on efficacy of benefits in other disorders rather than evidence from randomized controlled clinical trials. Symptoms remain difficult to manage. Several DMA drugs are currently being evaluated as either repurposing candidates or novel small molecules. Continued improvement in methodological aspects including development of DLB-specific outcome measures and biomarkers is needed to move the field of DMA drug development forward.

Global rural health disparities in Alzheimer's disease and related dementias: State of the science.

INTRODUCTION: Individuals living in rural communities are at heightened risk for Alzheimer's disease and related dementias (ADRD), which parallels other persistent place-based health disparities. Identifying multiple potentially modifiable risk factors specific to rural areas that contribute to ADRD is an essential first step in understanding the complex interplay between various barriers and facilitators. METHODS: An interdisciplinary, international group of ADRD researchers convened to address the overarching question of: "What can be done to begin minimizing the rural health disparities that contribute uniquely to ADRD?" In this state of the science appraisal, we explore what is known about the biological, behavioral, sociocultural, and environmental influences on ADRD disparities in rural settings. RESULTS: A range of individual, interpersonal, and community factors were identified, including strengths of rural residents in facilitating healthy aging lifestyle interventions. DISCUSSION: A location dynamics model and ADRD-focused future directions are offered for guiding rural practitioners, researchers, and policymakers in mitigating rural disparities. HIGHLIGHTS: Rural residents face heightened Alzheimer's disease and related dementia (ADRD) risks and burdens due to health disparities. Defining the unique rural barriers and facilitators to cognitive health yields insight. The strengths and resilience of rural residents can mitigate ADRD-related challenges. A novel "location dynamics" model guides assessment of rural-specific ADRD issues.

Mindfulness and Care Experience in Family Caregivers of Persons Living with Dementia.

Background: Greater mindfulness, the practice of awareness and living in the moment without judgement, has been linked to positive caregiving outcomes in dementia caregivers and its impact attributed to greater decentering and emotion regulation abilities. Whether the impact of these mindfulness-based processes varies across caregiver subgroups is unclear. Objective: Analyze cross-sectional associations between mindfulness and caregiver psychosocial outcomes, considering different caregiver and patient characteristics. Methods: A total of 128 family caregivers of persons living with Alzheimer's disease and related disorders were assessed on several mindfulness measures (i.e., global; decentering, positive emotion regulation, negative emotion regulation) and provided self-reported appraisals of caregiving experience; care preparedness; confidence, burden, and depression/anxiety. Bivariate relationships between mindfulness and caregiver outcomes were assessed with Pearson's correlations and stratified by caregiver (women versus men; spouse versus adult child) and patient (mild cognitive impairment (MCI) versus Dementia; AD versus dementia with Lewy bodies; low versus high symptom severity) characteristics. Results: Greater mindfulness was associated with positive outcomes and inversely associated with negative outcomes. Stratification identified specific patterns of associations across caregiver groups. Significant correlations were found between all mindfulness measures and caregiving outcomes in male and MCI caregivers while the individual mindfulness component of positive emotion regulation was significantly correlated to outcomes in most caregiver groups. Conclusion: Our findings support a link between caregiver mindfulness and improved caregiving outcomes and suggest directions of inquiry into whether the effectiveness of dementia caregiver-support interventions may be improved by targeting specific mindfulness processes or offering a more inclusive all-scope approach depending on individual caregiver or patient characteristics.

Vascular Cognitive Impairment (VCI).

Vascular cognitive impairment (VCI) is predominately caused by vascular risk factors and cerebrovascular disease. VCI includes a broad spectrum of cognitive disorders, from mild cognitive impairment to vascular dementia caused by ischemic or hemorrhagic stroke, and vascular factors alone or in a combination with neurodegeneration including Alzheimer's disease (AD) and AD-related dementia. VCI accounts for at least 20-40% of all dementia diagnosis. Growing evidence indicates that cerebrovascular pathology is the most important contributor to dementia, with additive or synergistic interactions with neurodegenerative pathology. The most common underlying mechanism of VCI is chronic age-related dysregulation of CBF, although other factors such as inflammation and cardiovascular dysfunction play a role. Vascular risk factors are prevalent in VCI and if measured in midlife they predict cognitive impairment and dementia in later life. Particularly, hypertension, high cholesterol, diabetes, and smoking at midlife are each associated with a 20 to 40% increased risk of dementia. Control of these risk factors including multimodality strategies with an inclusion of lifestyle modification is the most promising strategy for treatment and prevention of VCI. In this review, we present recent developments in age-related VCI, its mechanisms, diagnostic criteria, neuroimaging correlates, vascular risk determinants, and current intervention strategies for prevention and treatment of VCI. We have also summarized the most recent and relevant literature in the field of VCI.

The Resilience Index: A Quantifiable Measure of Brain Health and Risk of Cognitive Impairment and Dementia.

BACKGROUND: There is increasing interest in lifestyle modification and integrative medicine approaches to treat and/or prevent mild cognitive impairment (MCI) and Alzheimer's disease and related dementias (ADRD). OBJECTIVE: To address the need for a quantifiable measure of brain health, we created the Resilience Index (RI). METHODS: This cross-sectional study analyzed 241 participants undergoing a comprehensive evaluation including the Clinical Dementia Rating and neuropsychological testing. Six lifestyle factors including physical activity, cognitive activity, social engagements, dietary patterns, mindfulness, and cognitive reserve were combined to derive the RI (possible range of scores: 1-378). Psychometric properties were determined. RESULTS: The participants (39 controls, 75 MCI, 127 ADRD) had a mean age of 74.6±9.5 years and a mean education of 15.8±2.6 years. The mean RI score was 138.2±35.6. The RI provided estimates of resilience across participant characteristics, cognitive staging, and ADRD etiologies. The RI showed moderate-to-strong correlations with clinical and cognitive measures and very good discrimination (AUC: 0.836; 95% CI: 0.774-0.897) between individuals with and without cognitive impairment (diagnostic odds ratio = 8.9). Individuals with high RI scores (> 143) had better cognitive, functional, and behavioral ratings than individuals with low RI scores. Within group analyses supported that controls, MCI, and mild ADRD cases with high RI had better cognitive, functional, and global outcomes than those with low RI. CONCLUSION: The RI is a brief, easy to administer, score and interpret assessment of brain health that incorporates six modifiable protective factors. Results from the RI could provide clinicians and researchers with a guide to develop personalized prevention plans to support brain health.

A Modified CAIDE Risk Score as a Screening Tool for Cognitive Impairment in Older Adults.

BACKGROUND: Although an efficacious dementia-risk score system, Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) was derived using midlife risk factors in a population with low educational attainment that does not reflect today's US population, and requires laboratory biomarkers, which are not always available. OBJECTIVE: Develop and validate a modified CAIDE (mCAIDE) system and test its ability to predict presence, severity, and etiology of cognitive impairment in older adults. METHODS: Population consisted of 449 participants in dementia research (N = 230; community sample; 67.9±10.0 years old, 29.6%male, 13.7±4.1 years education) or receiving dementia clinical services (N = 219; clinical sample; 74.3±9.8 years old, 50.2%male, 15.5±2.6 years education). The mCAIDE, which includes self-reported and performance-based rather than blood-derived measures, was developed in the community sample and tested in the independent clinical sample. Validity against Framingham, Hachinski, and CAIDE risk scores was assessed. RESULTS: Higher mCAIDE quartiles were associated with lower performance on global and domain-specific cognitive tests. Each one-point increase in mCAIDE increased the odds of mild cognitive impairment (MCI) by up to 65%, those of AD by 69%, and those for non-AD dementia by > 85%, with highest scores in cases with vascular etiologies. Being in the highest mCAIDE risk group improved ability to discriminate dementia from MCI and controls and MCI from controls, with a cut-off of ≥7 points offering the highest sensitivity, specificity, and positive and negative predictive values. CONCLUSION: mCAIDE is a robust indicator of cognitive impairment in community-dwelling seniors, which can discriminate well between dementia severity including MCI versus controls. The mCAIDE may be a valuable tool for case ascertainment in research studies, helping flag primary care patients for cognitive testing, and identify those in need of lifestyle interventions for symptomatic control.

The Cognitive & Leisure Activity Scale (CLAS): A new measure to quantify cognitive activities in older adults with and without cognitive impairment.

INTRODUCTION: Potentially modifiable dementia risk factors include diet and physical and cognitive activity. However, there is a paucity of scales to quantify cognitive activities. To address this, we developed the Cognitive & Leisure Activity Scale (CLAS). METHODS: The CLAS was validated in 318 consecutive individuals with and without cognitive impairment. Psychometric properties were compared with sample characteristics, disease stage, and etiology. RESULTS: The CLAS has very good data quality (Cronbach alpha: 0.731; 95% confidence interval: 0.67-0.78). CLAS scores correlated with gold standard measures of cognition, function, physical functionality, behavior, and caregiver burden. CLAS scores were positively correlated with other resilience factors (eg, diet, physical activity) and negatively correlated with vulnerability factors (eg, older age, frailty). DISCUSSION: The CLAS is a brief inventory to estimate dosage of participation in cognitive activities. The CLAS could be used in clinical care to enhance cognitive activity or in research to estimate dosage of activities prior to an intervention.

Characterization of dementia with Lewy bodies (DLB) and mild cognitive impairment using the Lewy body dementia module (LBD-MOD).

INTRODUCTION: The National Institute on Aging Alzheimer's Disease Research Center program added the Lewy body dementia module (LBD-MOD) to the Uniform Data Set to facilitate LBD characterization and distinguish dementia with Lewy bodies (DLB) from Alzheimer's disease (AD). We tested the performance of the LBD-MOD. METHODS: The LBD-MOD was completed in a single-site study in 342 participants: 53 controls, 78 AD, and 110 DLB; 79 mild cognitive impairment due to AD (MCI-AD); and 22 MCI-DLB. RESULTS: DLB differed from AD in extrapyramidal symptoms, hallucinations, apathy, autonomic features, REM sleep behaviors, daytime sleepiness, cognitive fluctuations, timed attention tasks, and visual perception. MCI-DLB differed from MCI-AD in extrapyramidal features, mood, autonomic features, fluctuations, timed attention tasks, and visual perception. Descriptive data on LBD-MOD measures are provided for reference. DISCUSSION: The LBD-MOD provided excellent characterization of core and supportive features to differentiate DLB from AD and healthy controls while also characterizing features of MCI-DLB.

The Effect of Baseline Patient and Caregiver Mindfulness on Dementia Outcomes.

BACKGROUND: Mindfulness is the practice of awareness and living in the present moment without judgment. Mindfulness-based interventions may improve dementia-related outcomes. Before initiating interventions, it would be beneficial to measure baseline mindfulness to understand targets for therapy and its influence on dementia outcomes. OBJECTIVE: This cross-sectional study examined patient and caregiver mindfulness with patient and caregiver rating scales and patient cognitive performance and determined whether dyadic pairing of mindfulness influences patient outcomes. METHODS: Individuals (N = 291) underwent comprehensive evaluations, with baseline mindfulness assessed using the 15-item Applied Mindfulness Process Scale (AMPS). Correlation, regression, and mediation models tested relationships between patient and caregiver mindfulness and outcomes. RESULTS: Patients had a mean AMPS score of 38.0±11.9 and caregivers had a mean AMPS score of 38.9±11.5. Patient mindfulness correlated with activities of daily living, behavior and mood, health-related quality of life, subjective cognitive complaints, and performance on episodic memory and attention tasks. Caregiver mindfulness correlated with preparedness, care confidence, depression, and better patient cognitive performance. Patients in dyads with higher mindfulness had better cognitive performance, less subjective complaints, and higher health-related quality of life (all p-values<0.001). Mindfulness effects on cognition were mediated by physical activity, social engagement, frailty, and vascular risk factors. CONCLUSION: Higher baseline mindfulness was associated with better patient and caregiver outcomes, particularly when both patients and caregivers had high baseline mindfulness. Understanding the baseline influence of mindfulness on the completion of rating scales and neuropsychological test performance can help develop targeted interventions to improve well-being in patients and their caregivers.

Detection of Mild Cognitive Impairment and Alzheimer's Disease using Dual-task Gait Assessments and Machine Learning.

OBJECTIVE: Early detection of mild cognitive impairment (MCI) and Alzheimer's disease (AD) can increase access to treatment and assist in advance care planning. However, the development of a diagnostic system that d7oes not heavily depend on cognitive testing is a major challenge. We describe a diagnostic algorithm based solely on gait and machine learning to detect MCI and AD from healthy. METHODS: We collected "single-tasking" gait (walking) and "dual-tasking" gait (walking with cognitive tasks) from 32 healthy, 26 MCI, and 20 AD participants using a computerized walkway. Each participant was assessed with the Montreal Cognitive Assessment (MoCA). A set of gait features (e.g., mean, variance and asymmetry) were extracted. Significant features for three classifications of MCI/healthy, AD/healthy, and AD/MCI were identified. A support vector machine model in a one-vs.-one manner was trained for each classification, and the majority vote of the three models was assigned as healthy, MCI, or AD. RESULTS: The average classification accuracy of 5-fold cross-validation using only the gait features was 78% (77% F1-score), which was plausible when compared with the MoCA score with 83% accuracy (84% F1-score). The performance of healthy vs. MCI or AD was 86% (88% F1-score), which was comparable to 88% accuracy (90% F1-score) with MoCA. CONCLUSION: Our results indicate the potential of machine learning and gait assessments as objective cognitive screening and diagnostic tools. SIGNIFICANCE: Gait-based cognitive screening can be easily adapted into clinical settings and may lead to early identification of cognitive impairment, so that early intervention strategies can be initiated.

The Effect of Sociodemographics, Physical Function, and Mood on Dementia Screening in a Multicultural Cohort.

PURPOSE: To assess age, sex, race and ethnicity disparities in cognitive function in community-dwelling older adults and identify factors that contribute to these disparities. PATIENTS AND METHODS: Cognitive performance (global and domain-specific) and self-reported cognitive function were compared among Black (N=57), Hispanic (N=139), and White (N=108) older adults. The impact of socioeconomic status (SES), physical functionality, and mood indicators was assessed with a combination of hierarchical general linear models and mediation analysis. RESULTS: Poorer cognitive performance and higher levels of impairment were found in older adults from racial and ethnic backgrounds. The contribution of lower SES to the observed racial and ethnic disparities in objective cognitive performance was 33% in Hispanics and about 20% in Blacks, while poorer physical functionality explained over half of the differences between Black and White participants. Higher self-reported cognitive impairment in minorities was explained by lower SES and higher depressive symptoms in Hispanics but not in Blacks. CONCLUSION: Performance on objective memory testing and self-reported cognition are greatly influenced by relevant biological, sociodemographic and medical variables. Dementia screening programs should be tailored to individual sociodemographic groups based on contributors that are specific to each group.

Intention to Be Screened for Alzheimer's Disease in Nondemented Older Adults: Integrated Behavioral Model and Self-Efficacy as Mediation Effect.

The study explored factors associated with intention to be screened for Alzheimer's disease (AD). The study also examined whether self-efficacy mediates the relationship between knowledge about screening and the intention to be screened for AD. A population-based, random-digit dialing survey was performed and 1,043 responses were collected from a sample of nondemented persons (50 years or older) living in urban, suburban, and rural areas in a Midwestern state. The findings showed that participants who were younger and who had higher levels of (a) perceived benefits and barriers, (b) social support, and (c) self-efficacy reported higher levels of intention to be screened for AD. Older adults with positive life orientation reported greater intention to be screened for AD, whereas depressed participants were more likely to report a plan to be screened for AD. Self-efficacy mediated the relationship between knowledge about screening and intention to be screened. Older adults were more likely to report intention to be screened when they had positive attitudes about the screen and believed that they could receive the screen. The intention to be screened for AD could serve public awareness by defining effective ways to assist older adults to seek a cognitive screen.

The positive and negative appraisals of caregiving (PANAC) scale: A new measure to examine the caregiving experience in Alzheimer's disease and related dementias.

INTRODUCTION: Dementia caregiving is often examined as a monolithic experience describing the challenges caregivers face, exploring one construct at a time, with little research on the positive experiences of caregiving. To address this, we developed the Positive and Negative Appraisals of Caregiving (PANAC) scale. METHODS: PANAC was validated in 253 patient-caregiver dyads. Factor analyses revealed a two-factor solution: Positive Appraisals (PAs) and Negative Appraisals (NAs). Psychometric properties were compared with patient and caregiver characteristics and outcomes, disease stage, and etiology. RESULTS: Internal consistency was good with Cronbach's alpha: 0.82 NA and 0.80 PA (P  < 0.001). NA correlated with patient and caregiver characteristics, whereas PA correlated only with caregiver characteristics. The PA/NA ratio could be used to capture change due to an intervention. DISCUSSION: The PANAC scale is a useful measure of the overall caregiver experience accounting for negative and positive experiences and may be used to tailor support to individual caregivers.

The Number Symbol Coding Task: A brief measure of executive function to detect dementia and cognitive impairment.

INTRODUCTION: Alzheimer's disease and related dementias (ADRD) affect over 5.7 million Americans and over 35 million people worldwide. Detection of mild cognitive impairment (MCI) and early ADRD is a challenge to clinicians and researchers. Brief assessment tools frequently emphasize memory impairment, however executive dysfunction may be one of the earliest signs of impairment. To address the need for a brief, easy-to-score, open-access test of executive function for use in clinical practice and research, we created the Number Symbol Coding Task (NSCT). METHODS: This study analyzed 320 consecutive patient-caregiver dyads who underwent a comprehensive evaluation including the Clinical Dementia Rating (CDR), patient and caregiver versions of the Quick Dementia Rating System (QDRS), caregiver ratings of behavior and function, and neuropsychological testing, with a subset undergoing volumetric magnetic resonance imaging (MRI). Estimates of cognitive reserve were calculated using education, combined indices of education and occupation, and verbal IQ. Psychometric properties of the NSCT including data quality, data distribution, floor and ceiling effects, construct and known-groups validity, discriminability, and clinical profiles were determined. RESULTS: The patients had a mean age of 75.3±9.2 years (range 38-98y) with a mean education of 15.7±2.8 years (range 6-26y) of education. The patients had a mean CDR-SB of 4.8±4.7 (range 0-18) and a mean MoCA score of 18.6±7.1 (range 1-30). The mean NSCT score was 30.1±13.8 and followed a normal distribution. All healthy controls and MCI cases were able to complete the NSCT. The NSCT showed moderate-to-strong correlations with clinical and neuropsychological measures with the strongest association (all p's < .001) for measures with executive components (e.g., Judgement and Problem Solving box of the CDR, Decision Making and Problem Solving domain of the QDRS, Trailmaking B, and Cognigram Attention and Executive Composite Scores). Women slightly outperformed men, and individuals with lower educational attainment and lower education-occupation indices had lower NSCT scores. Decreasing NSCT scores corresponded to older age, worse cognitive scores, higher CDR sum of boxes scores, worse caregiver ratings of function and behavior, worse patient and informant QDRS ratings, and smaller hippocampal volumes and hippocampal occupancy scores. The NSCT provided excellent discrimination (AUC: .866; 95% CI: .82-.91) with a cut-off score of 36 providing the best combination of sensitivity (0.880) and specificity (0.759). Combining the NSCT with patient QDRS and caregiver QDRS ratings improved discrimination (AUC: .908; 95% CI: .87-.94). DISCUSSION: The NSCT is a brief, 90-second executive task that incorporates attention, planning and set-switching that can be completed by individuals into the moderate-to-severe stages of dementia. The NSCT may be a useful tool for dementia screening, case-ascertainment in epidemiological or community-based ADRD studies, and in busy primary care settings where time is limited. Combining the NSCT with a brief structured interview tool such as the QDRS may provide excellent power to detect cognitive impairment. The NSCT performed well in comparison to standardized scales of a comprehensive cognitive neurology evaluation across a wide array of sociodemographic variables in a brief fashion that could facilitate its use in clinical care and research.

Dual-Task Gait Assessment and Machine Learning for Early-detection of Cognitive Decline.

Alzheimer's disease (AD) affects approximately 30 million people worldwide, and this number is predicted to triple by 2050 unless further discoveries facilitate the early detection and prevention of the disease. Computerized walkways for simultaneous assessment of motor-cognitive performance, known as a dual-task assessment, has been used to associate changes in gait characteristics to mild cognitive impairment (MCI) with early-stage disease. However, to our best knowledge, there is no validated method to detect MCI using the collective analysis of these gait characteristics. In this paper, we develop a machine learning approach to analyze the gait data from the dual-task assessment in order to detect subjects with cognitive impairment from healthy individuals. We collected dual-task gait data from a computerized walkway of a total of 92 subjects with 31 healthy control (HC) and 61 MCI. Using support vector machine (SVM) and gradient tree boosting, we developed a classifier to differentiate MCI from HC subjects and compared the results with a paper-based questionnaire assessment that has been commonly used in clinical practice. SVM provided the highest accuracy of 77.17% with 81.97% sensitivity and 67.74% specificity. Our results indicate the potential of machine learning + dual-task assessment to enable early diagnosis of cognitive decline before it advances to dementia and AD, so that early intervention or prevention strategies can be initiated.