bluuebunny/crossref_metadata_2025_split huggingface dataset:
Patients living alone with dementia: course of health‐related quality of life over time and the impact of collaborative care in Germany
Authors: Michalowsky Bernhard , Platen Moritz 
Published: December 2023

AbstractBackgroundLittle is known about the progression of patient‐reported outcomes in community‐dwelling people living alone with cognitive impairment (PLACI) and the efficacy and cost‐effectiveness of collaborative care models, initiating evidence‐based care and support for this vulnerable subgroup. Thus, this study aims to demonstrate the longitudinal course of patient‐reported outcomes and assess the efficacy and cost‐effectiveness of collaborative care in PLACI compared to those not living alone.MethodDelpHi‐MV (Dementia: Life‐ and person‐centred help in Mecklenburg‐Western Pomerania) is a German general practitioner‐based, randomized controlled intervention trial. The present analyses are based on longitudinal (24 months) data of 352 patients (≥70 years, community‐dwelling). Patient‐reported outcomes were assessed using the SF‐12, dividing patients' health‐related quality of life (HRQoL) into mental and physical parts. Descriptive statistics were used to demonstrate HRQoL longitudinally. The efficacy and cost‐effectiveness of the collaborative care model were assessed by multivariate regression models and by cost‐effectiveness‐acceptability curves, separately for PLACI vs those living not alone.ResultN = 178 (51%) patients were living alone and more likely female (73% vs 49%) and older (81 years vs 79 years) than those living not alone. Considering PLACI only, mental and physical HRQoL decreased over time (‐2.4 and ‐2.2) without collaborative care and increased (+1.4 and +2.5) with collaborative care. Multivariate models confirmed the significant positive impact of collaborative care on mental HRQoL (b = +3.1, p = 0.032) over time. The probability of cost‐effectiveness was 96% at a willingness‐to‐pay of 40,000€ /QALY for PLACI, much higher than in those living not alone (26%).ConclusionHRQoL would decrease over time in PLACI. Collaborative care could improve patients' everyday lives, especially mental health. Thus, when such models are implemented, health policy could gain the highest cost savings and effects on HRQoL in PLACI.
Rural‐urban disparities in the consumption of fruits and vegetables and cognitive performance in Brazil
Authors: Gonçalves Natalia G , Bertola Laiss , Ferri Cleusa P , Suemoto Claudia Kimie 
Published: December 2023

AbstractBackgroundAdherence to a healthy diet has been associated with lower risk of dementia and cognitive decline. Furthermore, low consumption of fruits and vegetables has been associated with lower cognitive performance. Rural Brazilians have several factors related to poor diet quality, such as low education and income.Additionally, rural residents of Latin America face a higher burden of dementia compared to urban residents. However, differences in the association of fruit and vegetable consumption with cognitive performance in urban and rural areas have not been explored. The aim of this study was to investigate the association of fruit and vegetable consumption with cognitive performance in urban and rural areas in a nationally representative sample of Brazilian older adults.MethodThe sample included 9,412 adults 50 years or older from the 2015 wave of the Brazilian Longitudinal Study of Aging. Consumption of fruits and vegetables was measured using a food frequency questionnaire. Cognitive performance was evaluated using the immediate recall, late recall, semantic verbal fluency, prospective memory, semantic memory, and temporal orientation. A global composite z‐score was derived from these tests. Association of consumption of fruits and vegetables with cognitive performance was evaluated using linear regression, adjusted for sociodemographic, lifestyle, and clinical variables.ResultIn 6,743 participants (mean age 61.3±8.9 y, 53% women, 43% White, and 15% from rural areas), the mean intake of fruits and vegetables was 2.0±1.3 portions a day (Table 1). Higher consumption of fruits and vegetables was associated with better episodic (β = 0.021, 95% CI = 0.004;0.037, p = 0.013) and semantic memory (β = 0.037, 95% CI = 0.019;0.055, p<0.001), verbal fluency (β = 0.035, 95% CI = 0.018;0.053, p<0.001), and global cognition (β = 0.032, 95% CI = 0.016;0.048, p<0.001) performances (Table 2). There was a modifying effect of urban/rural area of residency (p for interaction <0.001). Stratified analysis showed that consumption of fruits and vegetables was associated with better episodic and semantic memory, verbal fluency, and global cognition performances in participants who resided in urban, but not rural areas (Table 3).ConclusionConsumption of fruits and vegetables was associated with cognitive performance in urban, but not in rural areas in Brazil, possibly due to socioeconomic disadvantages and lower access to healthcare.
Location Dynamics in Northern Rural Minnesota: Barriers and Facilitators for ADRD Diagnosis and Care
Authors: Warry Wayne , Fergen Joshua , Soderlund Patricia R , Cruikshank Kirsten R , Jacklin Kristen 
Published: December 2023

AbstractBackgroundThe location dynamics of rural northern Minnesota involve shifting economic structures, demographic transitions, and diverse physical environments that affect access to Alzheimer’s Disease and Related Dementias (ADRD) diagnosis, care and health services.MethodWe conducted semi‐structured interviews with 56 key informants including health care providers (n = 7), health leaders and administrators (n = 10), social workers (n = 12), formal and informal caregivers (n = 19), and healthy older adults (n = 8) across northern Minnesota concerning the unique protective and risk factors and the socio‐cultural context of ADRD in the region. Interviews were transcribed and thematically coded in QSR NVivo using a lived experience framework.ResultOur analysis reveals that winter weather, depopulation trends, and lack of healthcare facilities and services and aspects of rural culture create multi‐faceted challenges to healthy aging and ADRD diagnosis and care. Rural residents often rely on social capital, community institutions, volunteerism, and families to compensate for the lack of medical services. Rural people without social or financial resources may have to relocate to different communities to receive care and services. This lack of access to local care influences stigma and fear of dementia diagnoses in rural areas. These experiences shape cultural understandings of ADRD, and health seeking behaviors, which can be characterized as hesitant and dismissive, creating additional challenges for community interventions.ConclusionThe location dynamics of rural northern Minnesota shape unique cultural understandings around ADRD diagnosis and care that limits the effectiveness of existing interventions and demands tailored approaches to overcome the lack of local resources, geographic distance, and winter weather constraints. Dense social networks, a culture of volunteerism, and community capacity to develop medical infrastructure are key assets used to overcome these challenges to ADRD care.
Digital and plasma biomarkers for an early diagnosis of Mild Cognitive Impairment and prodromal Alzheimer’s disease
Authors: Tort‐Merino Adrià , Sarto Jordi , Esteller Diana , Tarnanas Ioannis , Bügler Maximilian , Harms Robbert , Iulita M. Florencia , Santuccione Antonella , Ruiz‐García Raquel , Naranjo Laura , Martínez Neus Falgàs , Borrego‐Écija Sergi , Guillén Núria , Fernandez‐Villullas Guadalupe , Val‐Guardiola Andrea , Juncà‐Parella Jordi , Bosch Beatriz , Lladó Albert , Sanchez‐Valle Raquel , Balasa Mircea 
Published: December 2023

AbstractBackgroundThe application of blood‐based biomarkers for the identification of Alzheimer’s disease (AD) and the development of novel digital technologies as cognitive screening tests are critical to moving toward a reliable, more accessible early diagnosis. Our aim was to evaluate the diagnostic performance of a machine learning‐based cognitive assessment known as Altoida’s digital neuro‐signature (DNS) in patients with non‐degenerative mild cognitive impairment (ndMCI) and MCI due to AD (prodromal AD) and its association with CSF and plasma biomarkers.MethodAltoida’s MCI‐DNS is a 10‐minute cognitive test battery evaluating activities of daily living via motoric and augmented reality tasks. The test consists of placing and finding virtual objects in a real environment and its final score is obtained by weighting multi‐modal digital data features, such as hands’ micro‐movements, speed, reaction times, or navigation trajectories. We included 81 participants, classified according to their clinical status and CSF AD biomarker profile as: cognitively unimpaired controls, CTR (n = 10; age = 68.5±5.9; MMSE = 29.4±1.1), ndMCI (n = 25; age = 67.6±7.2; MMSE = 26.9±1.9) and prodromal AD (n = 46; age = 70.8±4.9; MMSE = 24.3±3.3). We further investigated a subsample of participants classified according to their plasma pTau181 levels as measured by SiMoA [cutoff = 1.37 pg/mL (Sarto et al., 2022)]: pTau181 negative (n = 27; age = 68.5±5.9; MMSE = 26.9±2.7) or pTau181 positive (n = 30; age = 70.3±5.5; MMSE = 24.1±3.7).ResultSignificant differences were found in MCI‐DNS scores between CTR group and ndMCI (F = 23.5; p<0.001) and prodromal AD (F = 114.4; p<0.001) groups. Also, ndMCI showed higher MCI‐DNS scores than the prodromal AD group (F = 4.53; p<0.05, Fig. 1). ROC curves showed an excellent diagnostic accuracy of the MCI‐DNS in the discrimination between CTR vs. ndMCI (AUC = 0.879) and CTR vs. prodromal AD (AUC = 0.975) (Fig. 1). Further analyses showed differences in MCI‐DNS scores between CSF Aβ42 negative and CSF Aβ42 positive (F = 18.9; p<0.001; Fig. 2), as well as between plasma pTau181 negative and plasma pTau181 positive (F = 6.16; p<0.01; Fig. 2). Finally, MCI DNS scores significantly correlated with CSF Aβ42, CSF Aβ42/pTau ratio, CSF neurofilament‐light chain (NfL) and plasma pTau181 concentrations (Fig. 3).ConclusionAltoida’s MCI‐DNS test allows excellent discrimination between CTR and patients with MCI. MCI‐DNS scores significantly correlate with CSF AD core biomarkers, biomarkers of neurodegeneration and blood‐based biomarkers (i.e., plasma pTau181).
Health System Initiatives: Catalysts for Change
Authors: Daven Morgan 
Published: December 2023

AbstractHealth systems hold the key to change for dementia care, representing the primary organizational structures through which meaningful change will occur. The Alzheimer’s Association, the world’s largest nonprofit organization dedicated to Alzheimer’s care, is committed to leading this change. Starting in July 2019, the Alzheimer’s Association launched a Health Systems Initiative to facilitate and enhance dementia care in the clinical setting.In the three years since its launch, this initiative has made hundreds of connections to health systems across the United States, offering proven solutions for improving health outcomes to more effectively provide care for people living with dementia. Twenty‐two regional health systems directors help increase awareness of resources for clinical training, with a focus on soliciting feedback from providers to better support their work.The goals of the initiative are to improve health measures, as well as quality of life measures, and promote ways to make it easier for clinicians to provide high quality care. The Alzheimer’s Association Health Systems Initiative provides technical assistance for quality improvement, solutions to advance system and practice changes, and an organizational structure to help disseminate best practices.Toolkits such as the KAER Model designed by the Gerontological Society of America, quality improvement models such as the Cognition in Primary Program at the University of Washington, and primary care models specialized in dementia care such as the Integrated Memory Care Program at Emory University are helping meet the complex care needs of individuals and families living with Alzheimer’s disease and related dementias.
Examining the reorganization of cognitive networks in subjective cognitive decline: A network analysis approach
Authors: Grunden Nicholas , Phillips Natalie 
Published: December 2023

AbstractBackgroundIndividuals with subjective cognitive decline (SCD) are at increased risk for developing dementia. However, research is still needed to identify what subtle cognitive changes in SCD might differentiate it from healthy aging. Network analysis has been used to model interrelationships between cognitive measures in healthy and clinically impaired older adults. These multivariate networks can capture a more holistic view of performance within and across cognitive domains, providing further insight into cognitive status. Studies of individuals with Alzheimer’s disease (AD) and mild cognitive impairment (MCI) have revealed distinct network characteristics compared to cognitively normal (CN) older adults. We employed this novel technique to determine whether networks in SCD show evidence of reorganization compared to CN individuals.MethodThis project examined the cognitive network of a sample group with SCD (n = 207, 151 females/56 males, M
Age = 71.6 ± 5.50, M
Education = 15.5 ± 3.40) and compared it with networks of CN (n = 122, 94 females/28 males, M
Age = 71.3 ± 6.08, M
Education = 15.6 ± 3.41), MCI (n = 210, 100 females/110 males, M
Age = 73.7 ± 6.55, M
Education = 15.1 ± 3.81), and AD (n = 79, 32 females/47 males, M
Age = 75.9 ± 7.33, M
Education = 15.3 ± 4.34) participants. Group networks were comprised of performance on various cognitive tests from COMPASS‐ND1 and CIMA‐Q2 aging studies. Networks were constructed as Gaussian graphical models with EBICglasso regularization applied to return sparser networks3.ResultStrength centrality indices (i.e., the absolute sum of partial correlations with other variables) suggest that measures of executive functioning become less influential while measures of long‐term memory increase their influence in SCD networks compared to CN networks, reflecting a shift from a common decrease in executive processing seen in normal aging to the episodic memory deficits characteristic of AD‐type cognitive decline.ConclusionThese findings not only differentiate SCD networks from CN networks, but also suggest the beginnings of cognitive reorganization towards MCI, supporting the view of SCD as a preclinical state in the AD continuum.
References:1. Chertkow et al. (2019), https://doi.org/10.1017/cjn.2019.272.	Belleville et al. (2019), https://doi.org/10.1016/j.dadm.2019.07.0033.	Epskamp & Fried (2018), https://doi.org/10.1037/met0000167
Virtual Design of the Brain Health Support Program and CAN‐THUMBS UP
Authors: Feldman Howard H. , Belleville Sylvie , Nygaard Haakon B. , Montero‐Odasso Manuel , Chertkow Howard ,   CCNA‐CAN‐THUMBS UP Study Group
Published: December 2023

AbstractBackgroundModifiable lifestyle factors account for ∼40% of the population attributable risk of dementia. This provides a strong rationale to develop remotely administered, personally tailored, home‐based interventions to reduce these risk factors. CAN‐THUMBS UP (CTU) is a fully remote program that has enrolled a cohort of individuals at risk of dementia, developed an interactive online educational intervention to address personalized risk factors and their modification, and supports an open platform trial to test and compare the efficacy of multidomain interventions. CTU is reaching a broader geography with potential for inclusion of more representative research populations and with an easier and less demanding participant experience.MethodCTU’s two research intervention programs include: a) The Brain Health Support Program (BHSP) with individuals at increased risk of dementia participating in the Brain Health PRO, a 12‐month online educational program which focuses on lifestyle and dementia risk factors; b) Synchronizing, Exercises and Remedies to Gain Cognition (SYNERGIC), a clinical trial intervention platform whose RCTs are investigating personalized multidomain interventions including those administered fully remotely. In SYNERGIC‐2, the BHPro educational intervention is used as an active control to a more personalized and tailored interventional approach. CTU programs are informed by initial pilot studies, have extensive community input, and incorporate innovative features including an interactive E‐consent platform, remote cognitive testing, wearable technology for sleep and activity monitoring, and home saliva collection for genomics.ResultTarget enrollment of 350 participants in BHSP has been rapidly achieved within 8 months utilizing geotargeted mailing and a coordinated national media campaign. Participants are recruited from all regions of Canada. Pilot studies demonstrated the feasibility of conducting a 100% remote study with high rates of participant satisfaction and completion rates across various virtual outcomes. Results of each of these components of the remote program will be detailed in this FRS.ConclusionCTU demonstrates the feasibility and high rate of participant acceptance in fully remote, comprehensive, online programs that include an educational intervention as well as fully remote, multi‐domain lifestyle intervention trials. The adoption of the various technologies has exceeded expectations and supports further clinical trial development with this approach.
Virtual Cognitive Testing‐ Neuropsychology, MyCogHealth and Cogniciti
Authors: Brewster Paul , Hofer Scott M , Jacobs Diane M. , Feldman Howard H. , Belleville Sylvie , Montero‐Odasso Manuel , Nygaard Haakon B. , Chertkow Howard ,   CCNA‐CAN‐THUMBS UP Study Group
Published: December 2023

AbstractBackgroundIt is increasingly possible to mobilize technology to more readily evaluate effects of lifestyle interventions. Past research has found virtual administration of neuropsychological testing yields comparable results to in‐person assessments. Self‐administered paradigms using app‐and web‐based assessments have likewise shown promise for measuring cognition longitudinally. The Brain Health Support Program (BHSP) investigates three highly scalable cognitive assessment approaches for use in dementia prevention trials.MethodThe BHSP includes 60‐minute traditional neuropsychological assessments administered remotely via videoconferencing by a study coordinator (baseline & 12‐mos.), 20‐minute self‐administered web‐based cognitive screening assessments with Cogniciti (baseline, 6, and 12‐mos.), and self‐administered ecological momentary assessments using the MyCogHealth app. An intensive measurement protocol is implemented with MyCogHealth whereby participants completed five assessment “bursts” in three‐month intervals. Each “burst” involved five‐minute cognitive testing sessions completed twice daily across seven days. Remote neuropsychological assessments and Cogniciti were completed at home on personal devices. Study phones were provided to those without a smartphone capable of operating MyCogHealth. A three‐month feasibility pilot was completed in early 2022.ResultThe BHSP Pilot found 100% compliance with remote neuropsychological assessments. Participants averaged 85% compliance with the MyCogHealth burst testing schedule. Self‐reported satisfaction with MyCogHealth ranged from 85% (comfort using the app) to 95% (satisfaction with burst testing schedule). During the BHSP Baseline, all participants successfully completed the remote neuropsychological assessment, Cogniciti, and MyCogHealth. 82% had a personal mobile device for operating MyCogHealth and the remainder were provided study phones. Mixed linear regression analyses modeling cognitive trajectories over the first six months of the BHSP in relation to modifiable dementia risk factors and change in health behaviours will be presented. Marginal and conditional R‐Squared values will characterize the relative sensitivity of the three virtual testing paradigms for measuring change.ConclusionAll three approaches to virtual cognitive testing were feasible for use with the BHSP cohort. Longitudinal results provide an evidence base to inform future applications of virtual testing in dementia prevention trials. Virtual cognitive testing could be stage‐setting by saving clinic visits and lowering trial costs that would in turn expand geographic diversity of participants and enable more interventions to be evaluated.
Using artificial intelligence (AI) to predict the conversion of cognitively unimpaired individuals to mild cognitive impairment
Authors: Rawi Alya AL , Hassan Nehal , Ardle Ríona Mc , Slight Sarah 
Published: December 2023

AbstractBackgroundMild cognitive impairment (MCI) is the objective decline in neurocognitive functioning, but without significant impairment of the individual’s ability to perform the usual instrumental activities of daily living (1). Diagnosing MCI can be done using a combination of different methods such as cognitive testing, structural neuroimaging, nuclear imaging, and cerebrospinal fluids, and plasma biomarkers. Some of these methods are invasive and expensive. Machine learning (ML) algorithms can be trained to predict the onset of MCI, using data from non‐invasive methods.MethodFour databases (WoS, MEDLINE, EMBASE, and CINAHL) were searched using search string of relevant terms (cognitive decline, artificial intelligence, prediction, and cognitively unimpaired). Articles that reportedtrained ML algorithms using non‐invasive predictors of MCI in cognitively healthy adults (≥ 18 years old) were included. The review was registered with PROSPERO (CRD42022379027) and PRISMA guidelines were followed. The Newcastle‐Ottawa Quality Assessment scale was used to assess the quality of studies.ResultOf the 1,098 articles identified and screened, 22 studies were included. 373 non‐invasive predictors of MCI were identified. The most prevalent predictors included: demographic information (age, sex, years in education), voice and speech parameters (sentence repeating, semantic fluency), gait parameters (velocity, cadence, step time), performance on cognitive tests (MMSE, K‐MoCA, CDR, AD8), eye‐movements (pupil diameter, saccade orientation) and Instrumental activity of daily livings (medication, finance management). The maximum and minimum number of predictors used to develop a ML algorithm was 121 and 2, respectively. The ML algorithms developed in the included studies had (average sensitivity and specificity of 77.90%±22 and 80.84%±18, respectively) exceeded the performance of algorithms that used invasive and expensive predictors such as nuclear imaging and neuroimaging which achieved accuracy scores (60%‐77%) (2).ConclusionNon‐invasive predictors can be used to train ML algorithms to predict the onset of MCI in cognitively healthy individuals with good accuracy scores exceeding 70%. These algorithms may can aid clinician decision making, thus setting early treatment plans, and allowing individuals to be involved in care planning before progression of MCI to dementia AD.