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IntroductionAlzheimer's disease and related disorders (ADRD) progressively impair cognitive function, prompting the need for early detection to mitigate its impact. Mild Cognitive Impairment (MCI) may signal an early cognitive decline due to ADRD. Thus, developing an accessible, non-invasive method for detecting MCI is vital for initiating early interventions to prevent severe cognitive deterioration.MethodsThis study explores the utility of analyzing gait patterns, a fundamental aspect of human motor behavior, on straight and oval paths for diagnosing MCI. Using a Kinect v.2 camera, we recorded the movements of 25 body joints from 25 individuals with MCI and 30 healthy older adults (HC). Signal processing, descriptive statistical analysis, and machine learning techniques were employed to analyze the skeletal gait data in both walking conditions.Results and discussionThe study demonstrated that both straight and oval walking patterns provide valuable insights for MCI detection, with a notable increase in identifiable gait features in the more complex oval walking test. The Random Forest model excelled among various algorithms, achieving an 85.50% accuracy and an 83.9% F-score in detecting MCI during oval walking tests. This research introduces a cost-effective, Kinect-based method that integrates gait analysis—a key behavioral pattern—with machine learning, offering a practical tool for MCI screening in both clinical and home environments.

BackgroundDiagnosing Dementia with Lewy Bodies (DLB) remains a challenge in clinical practice. The use of 123I-ioflupane (DaTscan™) SPECT imaging, which detects reduced dopamine transporter (DAT) uptake—a key biomarker in DLB diagnosis—could improve diagnostic accuracy. However, DAT imaging is underutilized despite its potential, contributing to delays and suboptimal patient management.MethodsThis review evaluates DLB diagnostic practices and challenges faced within the U.S. by synthesizing information from current literature, consensus guidelines, expert opinions, and recent updates on DaTscan FDA filings. It contrasts DAT SPECT with alternative biomarkers, provides recommendations for when DAT SPECT imaging may be indicated and discusses the potential of emerging biomarkers in enhancing diagnostic approaches.ResultsThe radiopharmaceutical 123I-ioflupane for SPECT imaging was initially approved in Europe (2000) and later in the US (2011) for Parkinsonism/Essential Tremor. Its application was extended in 2022 to include the diagnosis of DLB. DaTscan’s diagnostic efficacy for DLB, with its sensitivity, specificity, and predictive values, confirms its clinical utility. However, US implementation faces challenges such as insurance barriers, costs, access issues, and regional availability disparities.Conclusion123I-ioflupane SPECT Imaging is indicated for DLB diagnosis and differential diagnosis of Alzheimer’s Disease, particularly in uncertain cases. Addressing diagnostic obstacles and enhancing physician-patient education could improve and expedite DLB diagnosis. Collaborative efforts among neurologists, geriatric psychiatrists, psychologists, and memory clinic staff are key to increasing diagnostic accuracy and care in DLB management.

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.

Abstract Background Identifying individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer’s disease and related dementia disorders (ADRD) would facilitate the development of individualized prevention plans. We investigated the association between MCI and comorbidities of ADRD. We examined the predictive potential of these comorbidities for MCI risk determination using a machine learning algorithm. Methods Using a retrospective matched case-control design, 5185 MCI and 15,555 non-MCI individuals aged ≥50 years were identified from MarketScan databases. Predictive models included ADRD comorbidities, age, and sex. Results Associations between 25 ADRD comorbidities and MCI were significant but weakened with increasing age groups. The odds ratios (MCI vs non-MCI) in 50–64, 65–79, and ≥ 80 years, respectively, for depression (4.4, 3.1, 2.9) and stroke/transient ischemic attack (6.4, 3.0, 2.1). The predictive potential decreased with older age groups, with ROC-AUCs 0.75, 0.70, and 0.66 respectively. Certain comorbidities were age-specific predictors. Conclusions The comorbidity burden of MCI relative to non-MCI is age-dependent. A model based on comorbidities alone predicted an MCI diagnosis with reasonable accuracy.

Abstract INTRODUCTION We examined whether a combined measure of neighborhood greenspace and neighborhood median income was associated with white matter hyperintensity (WMH) and ventricle size changes. METHODS The sample included 1260 cognitively normal ≥ 65‐year‐olds with two magnetic resonance images (MRI; ≈ 5 years apart). WMH and ventricular size were graded from 0 (least) to 9 (most) abnormal (worsening = increase of ≥1 grade from initial to follow‐up MRI scans). The four‐category neighborhood greenspace–income measure was based on median neighborhood greenspace and income values at initial MRI. Multivariable logistic regression tested associations between neighborhood greenspace–income and MRI measures (worsening vs. not). RESULTS White matter grade worsening was more likely for those in lower greenspace–lower income neighborhoods than higher greenspace–higher income neighborhoods (odds ratio = 1.73; 95% confidence interval = 1.19–2.51). DISCUSSION The combination of lower neighborhood income and lower greenspace may be a risk factor for worsening white matter grade on MRI. However, findings need to be replicated in more diverse cohorts. HIGHLIGHTS Population‐based cohort of older adults (≥ 65 years) with greenspace and MRI data Combined measure of neighborhood greenspace and neighborhood income at initial MRI MRI outcomes included white matter hyperintensities (WMH) and ventricular size Longitudinal change in MRI outcomes measured approximately 5 years apart Worsening WMH over time more likely for lower greenspace‐lower income neighborhoods

Abstract Background For screening and distinguishing between mild neurocognitive disorder (mNCD) and normal cognitive age-related changes in primary care centers, a simple and practical tool is necessary. Therefore, this study aims to determine the validity and reliability of the Farsi version of the Ascertain Dementia 8-item (AD8-F) informant interview in patients with mNCD. Methods This is a study of the psychometric properties of the Farsi AD8. The participants include sixty informant-patient dyads with mNCD and sixty controls with normal cognition. The AD8 was compared to the mini-mental state examination (MMSE) and the Mini-Cog. As a gold standard, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria for mNCD was used. The reliability was measured using internal consistency and test-retest. Validity was assessed by evaluating the content, concurrent, and construct validity. Data were analyzed via Cronbach’s α, Pearson correlation, independent t-test, and analysis of variance (ANOVA) and area under the curve (AUC) by statistical package for the social sciences (SPSS) v.23. Results Cronbach’s α was 0.71. Test-retest reproducibility was 0.8. The AD8 had inverse correlations with the Mini-Cog (r = − 0.70, P 2. Sensitivity and specificity were 80 and 83%, respectively. The positive predictive value was 83%. The negative predictive value was 81%. Conclusion Our results suggest that this tool can be used as a screening tool to detect a mild neurocognitive disorder in primary care centers.

Abstract Background Early detection of Alzheimer’s disease and related dementias (ADRD) in a primary care setting is challenging due to time constraints and stigma. The implementation of scalable, sustainable, and patient-driven processes may improve early detection of ADRD; however, there are competing approaches; information may be obtained either directly from a patient (e.g., through a questionnaire) or passively using electronic health record (EHR) data. In this study, we aim to identify the benefit of a combined approach using a pragmatic cluster-randomized clinical trial. Methods We have developed a Passive Digital Marker (PDM), based on machine learning algorithms applied to EHR data, and paired it with a patient-reported outcome (the Quick Dementia Rating Scale or QDRS) to rapidly share an identified risk of impairment to a patient’s physician. Clinics in both south Florida and Indiana will be randomly assigned to one of three study arms: 1200 patients in each of the two populations will be administered either the PDM, the PDM with the QDRS, or neither, for a total of 7200 patients across all clinics and populations. Both incidence of ADRD diagnosis and acceptance into ADRD diagnostic work-up regimens is hypothesized to increase when patients are administered both the PDM and QDRS. Physicians performing the work-up regimens will be blind to the study arm of the patient. Discussion This study aims to test the accuracy and effectiveness of the two scalable approaches (PDM and QDRS) for the early detection of ADRD among older adults attending primary care practices. The data obtained in this study may lead to national early detection and management program for ADRD as an efficient and beneficial method of reducing the current and future burden of ADRD, as well as improving the annual rate of newly documented ADRD in primary care practices. Trial registration ClinicalTrials.gov Identifier: NCT05231954 . Registered February 9, 2022.

Abstract The regulatory path for drug approval is increasingly well defined. Drugs for the treatment of Alzheimer disease (AD) need to show statistically significant benefit over placebo with respect to cognitive and functional measures, with the Clinical Dementia Rating scale and Alzheimer's Disease Assessment Scale–Cognitive Subscale being among the most often used instruments in AD clinical trials. In contrast, there are no validated instruments for use in clinical trials of drugs for the treatment of dementia with Lewy bodies. This poses challenges for drug development because the regulatory pathway to drug approval requires demonstrable efficacy measures. In December 2021, the Lewy Body Dementia Association advisory group met with representatives from the US Food and Drug Administration to discuss the lack of approved drugs and treatments, discernment of efficacy measures, and identification of biomarkers. Highlights The Lewy Body with Dementia Association convened a listening session with the US Food and Drug Administration on dementia with Lewy bodies (DLB) and clinical trial design. Gaps include DLB‐specific measures, alpha synuclein biomarkers, and coexisting pathologies. DLB clinical trial design should focus on clinical value and disease specificity.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease affecting cognitive and functional abilities. However, many patients presume lower cognitive or functional abilities because of aging and do not undergo clinical assessments until the symptoms become too advanced. Developing a low-cost and easy-to-use AD detection tool, which can be used in any clinical or non-clinical setting, can enable widespread AD assessments and diagnosis. This paper investigated the feasibility of developing such a tool to detect AD vs. healthy control (HC) from a simple balance and walking assessment called the Timed Up and Go (TUG) test. We collected joint position data of 47 HC and 38 AD subjects as they performed TUG in front of a Kinect V.2 camera. Our signal processing and statistical analyses provided a comprehensive analysis of balance and gait with 12 significant features for discriminating AD from HC after adjusting for age and the Geriatric Depression Scale. Using these features and a support vector machine classifier, our model classified the two groups with an average accuracy of 97.75% and an F-score of 97.67% for five-fold cross-validation and 98.68% and 98.67% for leave-one-subject out cross-validation. These results demonstrate the potential of our approach as a new quantitative complementary tool for detecting AD among older adults. Our work is novel as it presents the first application of Kinect V.2 camera and machine learning to provide a comprehensive and quantitative analysis of the TUG test to detect AD patients from HC. This study supports the feasibility of developing a low-cost and convenient AD assessment tool that can be used during routine checkups or even at home; however, future investigations could confirm its clinical diagnostic value in a larger cohort.

Abstract In 2019, the Lewy Body Dementia Association formed an Industry Advisory Council to bring together a collaborative group of stakeholders with the goal of accelerating clinical research into Lewy body dementia treatments. At the second annual meeting of the Industry Advisory Council, held virtually on June 18, 2020, the key members presented ongoing and planned efforts toward the council’s goals. The meeting also featured a discussion about the effects of the COVID-19 pandemic on Lewy body dementia clinical research, lessons learned from that experience, and how those lessons can be applied to the design and conduct of future clinical trials. This report provides a brief summary of the meeting proceedings with a focus on efforts to improve and adapt future Lewy body dementia clinical research.

Demonstrating a slowing in the rate of cognitive decline is a common outcome measure in clinical trials in Alzheimer’s disease (AD). Selection of cognitive endpoints typically includes modeling candidate outcome measures in the many, richly phenotyped observational cohort studies available. An important part of choosing cognitive endpoints is a consideration of improvements in performance due to repeated cognitive testing (termed “practice effects”). As primary and secondary AD prevention trials are comprised predominantly of cognitively unimpaired participants, practice effects may be substantial and may have considerable impact on detecting cognitive change. The extent to which practice effects in AD prevention trials are similar to those from observational studies and how these potential differences impact trials is unknown. In the current study, we analyzed data from the recently completed DIAN-TU-001 clinical trial (TU) and the associated DIAN-Observational (OBS) study. Results indicated that asymptomatic mutation carriers in the TU exhibited persistent practice effects on several key outcomes spanning the entire trial duration. Critically, these practice related improvements were larger on certain tests in the TU relative to matched participants from the OBS study. Our results suggest that the magnitude of practice effects may not be captured by modeling potential endpoints in observational studies where assessments are typically less frequent and drug expectancy effects are absent. Using alternate instrument forms (represented in our study by computerized tasks) may partly mitigate practice effects in clinical trials but incorporating practice effects as outcomes may also be viable. Thus, investigators must carefully consider practice effects (either by minimizing them or modeling them directly) when designing cognitive endpoint AD prevention trials by utilizing trial data with similar assessment frequencies.

Abstract Lewy body dementia (LBD), including dementia with Lewy bodies and Parkinson’s disease dementia, affects over a million people in the USA and has a substantial impact on patients, caregivers, and society. Symptomatic treatments for LBD, which can include cognitive, neuropsychiatric, autonomic, sleep, and motor features, are limited with only two drugs (cholinesterase inhibitors) currently approved by regulatory agencies for dementia in LBD. Clinical trials represent a top research priority, but there are many challenges in the development and implementation of trials in LBD. To address these issues and advance the field of clinical trials in the LBDs, the Lewy Body Dementia Association formed an Industry Advisory Council (LBDA IAC), in addition to its Research Center of Excellence program. The LBDA IAC comprises a diverse and collaborative group of experts from academic medical centers, pharmaceutical industries, and the patient advocacy foundation. The inaugural LBDA IAC meeting, held in June 2019, aimed to bring together this group, along with representatives from regulatory agencies, to address the topic of optimizing the landscape of LBD clinical trials. This review highlights the formation of the LBDA IAC, current state of LBD clinical trials, and challenges and opportunities in the field regarding trial design, study populations, diagnostic criteria, and biomarker utilization. Current gaps include a lack of standardized clinical assessment tools and evidence-based management strategies for LBD as well as difficulty and controversy in diagnosing LBD. Challenges in LBD clinical trials include the heterogeneity of LBD pathology and symptomatology, limited understanding of the trajectory of LBD cognitive and core features, absence of LBD-specific outcome measures, and lack of established standardized biologic, imaging, or genetic biomarkers that may inform study design. Demands of study participation (e.g., travel, duration, and frequency of study visits) may also pose challenges and impact trial enrollment, retention, and outcomes. There are opportunities to improve the landscape of LBD clinical trials by harmonizing clinical assessments and biomarkers across cohorts and research studies, developing and validating outcome measures in LBD, engaging the patient community to assess research needs and priorities, and incorporating biomarker and genotype profiling in study design.

Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most data repositories are integrated capabilities for semantic representation. The Arkansas Imaging Enterprise System (ARIES) is a research data management system which features integrated capabilities to support semantic representations of multi-modal data from disparate sources (imaging, behavioral, or cognitive assessments), across common image-processing stages (preprocessing steps, segmentation schemes, analytic pipelines), as well as derived results (publishable findings). These unique capabilities ensure greater reproducibility of scientific findings across large-scale research projects. The current investigation was conducted with three collaborating teams who are using ARIES in a project focusing on neurodegeneration. Datasets included magnetic resonance imaging (MRI) data as well as non-imaging data obtained from a variety of assessments designed to measure neurocognitive functions (performance scores on neuropsychological tests). We integrate and manage these data with semantic representations based on axiomatically rich biomedical ontologies. These instantiate a knowledge graph that combines the data from the study cohorts into a shared semantic representation that explicitly accounts for relations among the entities that the data are about. This knowledge graph is stored in a triple-store database that supports reasoning over and querying these integrated data. Semantic integration of the non-imaging data using background information encoded in biomedical domain ontologies has served as a key feature-engineering step, allowing us to combine disparate data and apply analyses to explore associations, for instance, between hippocampal volumes and measures of cognitive functions derived from various assessment instruments.

Abstract Introduction The objective was to develop and refine a version of the Prolonged‐Loss Grief‐12 (PG‐12) specific to caregivers of people living with dementia. Confirmatory factor analysis (CFA) was performed to test the fit of the data from the caregivers and to assess the factor structure of the PG‐12 in order to evaluate pre‐loss grief accurately by identifying relevant items and eliminating items that are not appropriate for caregivers of persons with dementia. Methods A total of 699 eligible caregivers of persons living with dementia (PLWDs) were recruited through relevant dementia associations and organizations. The e‐mail for recruitment provided potential participants with information about the study and detailed instructions on how to participate by following a link to the online survey. Secondary analysis was based on the survey data. CFA was conducted via the Full Information Maximum Likelihood estimation method to test the unidimensional model of PG‐12 in the study population. Standard procedures were used to establish the parameters in the factor loading, factor variance‐covariance, and uniqueness matrices. Results The initial model was modified to develop a better fitting model and to detect misfitting parameters in the PG‐12 by deleting irrelevant items for the PLWD's caregiver. The adjusted dementia‐specific 10‐item version (PG‐10‐D) had significantly improved fit indices. An overall assessment of fit indicated that the model adequately approximated the data. Factor loadings ranged from 0.53 to 0.85. Discussion We found that the dementia‐specific, unidimensional PG‐10‐D, modifying the original PG‐12, may be useful and parsimonious in assessing and quantifying pre‐loss grief in dementia caregivers. Future studies are needed to further test its validity and reliability. Highlights First study to evaluate a dementia‐specific 10‐item version (PG‐10‐D) of the Prolonged‐Loss Grief‐12 (PG‐12). PG‐10‐D is useful in assessing and quantifying pre‐loss grief in caregivers. PG‐10‐D could be an early identifier of caregivers at risk for pre‐loss grief. PG‐10‐D could be the standard measure for effective intervention for caregivers.

The Lewy Body Dementia Association (LBDA) held a virtual event, the LBDA Biofluid/Tissue Biomarker Symposium, on January 25, 2021, to present advances in biomarkers for Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLBs) and Parkinson's disease dementia (PDD). The meeting featured eight internationally known scientists from Europe and the United States and attracted over 200 scientists and physicians from academic centers, the National Institutes of Health, and the pharmaceutical industry. Methods for confirming and quantifying the presence of Lewy body and Alzheimer's pathology and novel biomarkers were discussed.

Abstract The first Lewy Body Dementia Association (LBDA) Research Centers of Excellence (RCOE) Investigator’s meeting was held on December 14, 2017, in New Orleans. The program was established to increase patient access to clinical experts on Lewy body dementia (LBD), which includes dementia with Lewy bodies (DLB) and Parkinson’s disease dementia (PDD), and to create a clinical trials-ready network. Four working groups (WG) were created to pursue the LBDA RCOE aims: (1) increase access to high-quality clinical care, (2) increase access to support for people living with LBD and their caregivers, (3) increase knowledge of LBD among medical and allied (or other) professionals, and (4) create infrastructure for a clinical trials-ready network as well as resources to advance the study of new therapeutics.

Alzheimer's disease (AD) is a progressive, neurodegenerative disease that creates complex challenges and a significant burden for patients and caregivers. Although underlying pathological changes due to AD may be detected in research studies decades prior to symptom onset, many patients in the early stages of AD remain undiagnosed in clinical practice. Increasing evidence points to the importance of an early and accurate AD diagnosis to optimize outcomes for patients and their families, yet many barriers remain along the diagnostic journey. Through a series of international working group meetings, a diverse group of experts contributed their perspectives to create a blueprint for a patient-centered diagnostic journey for individuals in the early stages of AD and an evolving, transdisciplinary care team. Here, we discuss key learnings, implications, and recommendations.

Aim. This study was aimed at validating the Filipino version of AD8 (AD8-P). Methods. Community-dwelling Filipino older persons aged ≥60 years, together with their informants, participated in this study. Psychologists independently interviewed the informants with AD8-P and administered the Filipino-validated Mini-Mental State Examination (MMSE-P) and Montreal Cognitive Assessment (MoCA-P) to the older persons. Neurologists and geriatrician conducted physical and neurological examination and Clinical Dementia Rating™ (CDR™) to determine cognitive diagnosis and were blinded with the results of AD8-P. Dementia was diagnosed based on DSM-IV-TR criteria. AD8-P discriminatory ability to screen for dementia was evaluated according to DSM-IV-TR diagnostic criteria for dementia. Results. A total of 366 community-dwelling Filipino older persons aged ≥60 years, 213 with normal cognition and 153 with dementia, and their informants were included in this study. Majority (90%) were at the mildest stage of dementia. Area under the receiver-operating-characteristic curve (AUROC) for AD8-P was 0.94 (95% CI 0.92 to 0.96), demonstrating excellent overall predictive power to screen for dementia. The optimal AD8-P cut-off score with best balance sensitivity (91.5%) and specificity (77.9%) was ≥3. Conclusion. AD8-P demonstrated good psychometric properties to screen for dementia, even at the earliest stage of cognitive decline.

Abstract 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.

Abstract Introduction A brief, easily calculated and interpretable index to assess vulnerability to developing cognitive impairment is needed in clinical practice and research. To address this, we developed the Vulnerability Index (VI) with the goal of identifying individuals possessing a high risk for cognitive impairment. Methods Twelve easily obtained sociodemographic, medical, and functional factors were used to develop the VI, with each selectively weighted based on factor analysis and predictive modeling. This cross‐sectional study examined 387 subject‐partner dyads. Results The VI was found to accurately discriminate between cognitively normal controls and participants with cognitive impairment (area under the curve [AUC]: 0.844; 95% confidence interval [CI]: 0.776‐0.913) and individuals scoring high on the VI (≥8) had worse health, functional, behavioral, cognitive, and quality of life ratings than those with lower scores. Discussion The VI could be used in screening asymptomatic individuals for risk of cognitive impairment and guiding the development of primary and secondary prevention plans.

Abstract 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

Abstract Background Hospitalized older persons with Alzheimer’s disease and related dementias are at greater risk for functional decline and increased care dependency after discharge due to a combination of intrinsic factors, environmental, policy, and care practices that restrict physical and cognitive activity, lack of family involvement and limited staff knowledge of dementia care. We have developed a theory-based intervention, Family centered Function-focused Care, that incorporates an educational empowerment model for family caregivers (FCGs) provided within a social-ecological framework to promote specialized care to patients with dementia during hospitalization and the 60-day post-acute period. Primary aims are to test the efficacy of the intervention in improving physical and cognitive recovery in hospitalized persons living with Alzheimer’s disease and related dementias (ADRD) and improving FCG preparedness and experiences. Method We will implement Family centered Function-focused Care in a cluster-randomized trial of 438 patient/FCG dyads in six hospital units randomized within three hospitals. We hypothesize that patients who receive the intervention will demonstrate better physical function, less delirium occurrence and severity, neuropsychiatric symptoms, and depression compared to those in the control condition (Education-only). We also hypothesize that FCGs enrolled in Family centered Function-focused Care will experience increased preparedness for caregiving, and less strain, burden, and desire to institutionalize, as compared to FCGs the control group. We will also examine the costs and relative cost savings associated with the intervention and will evaluate the cultural appropriateness of Family centered Function-focused Care for families from diverse backgrounds. Discussion Our theory-based intervention makes use of real-world applicable approaches in a novel and innovative way to change the paradigm of how we currently look at acute care and post-acute transitions in persons with ADRD. Trial registration ClinicalTrials.gov, ID: NCT03046121. Registered on 8 February 2017.

The terms “prevention” and “risk reduction” are often used interchangeably in medicine. There is considerable debate, however, over the use of these terms in describing interventions that aim to preserve cognitive health and/or delay disease progression of Alzheimer's disease (AD) for patients seeking clinical care. Furthermore, it is important to distinguish between Alzheimer's disease prevention and Alzheimer's dementia prevention when using these terms. While prior studies have codified research-based criteria for the progressive stages of AD, there are no clear clinical consensus criteria to guide the use of these terms for physicians in practice. A clear understanding of the implications of each term will help guide clinical practice and clinical research. The authors explore the semantics and appropriate use of the terms “prevention” and “risk reduction” as they relate to AD in clinical practice.

Abstract Introduction Dementia with Lewy bodies (DLB) is a challenge to diagnose, particularly outside of expert centers with long delays in diagnosis leading to significant burden to patients and caregivers. Although consensus criteria have excellent specificity, there is no standardized way to assess symptoms reducing sensitivity. We developed the Lewy body composite risk score (LBCRS) from autopsy‐verified cases to improve the ability to detect DLB in clinic and research populations. Methods The LBCRS was tested in a consecutive series of 256 patients compared with the clinical dementia rating and gold standard measures of cognition, motor symptoms, function, and behavior. Psychometric properties including floor and ceiling effects; concurrent, construct, and known‐groups validity; and internal consistency of the LBCRS were determined. Receiver operator characteristic (ROC) curves assessed the ability of LBCRS to differentiate (1) DLB from Alzheimer's disease (AD), (b) DLB from all dementia, and (c) mild cognitive impairment (MCI) due to DLB from MCI due to AD. The LBCRS was completed independent of the clinical evaluation. Results Mean LBCRS scores were significantly different between DLB and AD (6.1 ± 2.0 vs. 2.4 ± 1.3, P

Abstract Introduction Test the validity and reliability of the Quick Dementia Rating System (QDRS), a rapid dementia staging tool. Methods The QDRS was tested in 267 patient‐caregiver dyads compared with Clinical Dementia Ratings (CDR), neuropsychological testing, and gold standard measures of function, mood, and behavior. Psychometric properties including, item variability, floor and ceiling effects, concurrent and construct validity, and internal consistency were determined. The QDRS was used to derive an independent CDR and sum‐of‐boxes (SB). Interscale reliability between QDRS and CDR was tested using intraclass correlation coefficients (ICC). Area under the receiver operator characteristic curves (AUC) tested discrimination properties of QDRS across CDR stages. Results QDRS scores increased with higher CDR staging and poorer neuropsychological performance (Ps

Background: A clinical study team performing three multicultural dementia screening studies identified the need to improve data management practices and facilitate data sharing. A collaboration was initiated with librarians as part of the National Library of Medicine (NLM) informationist supplement program. The librarians identified areas for improvement in the studies’ data collection, entry, and processing workflows. Case Presentation: The librarians’ role in this project was to meet needs expressed by the study team around improving data collection and processing workflows to increase study efficiency and ensure data quality. The librarians addressed the data collection, entry, and processing weaknesses through standardizing and renaming variables, creating an electronic data capture system using REDCap, and developing well-documented, reproducible data processing workflows. Conclusions: NLM informationist supplements provide librarians with valuable experience in collaborating with study teams to address their data needs. For this project, the librarians gained skills in project management, REDCap, and understanding of the challenges and specifics of a clinical research study. However, the time and effort required to provide targeted and intensive support for one study team was not scalable to the library’s broader user community.

Background: Vocalizations are part of the spectrum of the ‘negative' behavioral and psychological symptoms of dementia (BPSD). We describe a patient with moderate-stage mixed dementia of Alzheimer's disease and cerebrovascular disease and a left orbitofrontal lesion exhibiting vocalization. The use of ‘redirection' has been demonstrated to be an effective nonpharmacological means of controlling BPSD, while reducing caregiver distress. Case Report: A 78-year-old right-handed African-American female presented with complaints of worsening memory and BPSD, causing significant caregiver distress. Throughout the evaluation, she constantly vocalized her son's name and made a continuous grunting noise, correlating with increased anxiety/agitation. We utilized a redirection technique, which achieved the immediate reduction of the vocalization symptoms. Caregiver psychoeducation was provided allowing them to use the redirection technique at home. Conclusions: In patients with dementia exhibiting negative symptoms of BPSD, using nonpharmacologic techniques (i.e. redirection) may be indicated. Psychotropic medications rarely address negative BPSD symptoms, while simultaneously decreasing patient's quality of life. Nonpharmacologic approaches are beneficial as first-line therapy for negative BPSD.

To understand the impact of memory loss on aging in place, this paper investigated dyads where one spouse had been diagnosed with memory loss. In-depth qualitative interviews were conducted with ten couples (N=20). Grounded theory methods were used to collect, code, and analyze data into themes. Data revealed consensus among and between dyads that it was best to focus on living, rather than what had been or might someday be lost. Nonetheless, differences according to gender and cognitive status (e.g., diagnosed or spouse) were reported. Given population aging, identifying the impact of gender roles and social norms on the potential for aging in place with memory loss is critical. Community services and care practices must be sensitive to the ways that couples prioritized and organized their relationship prior to diagnosis in order to encourage positive patterns of care between couples, foster successful adaptation to changing needs, and support in-home arrangements as long as possible.