ObjectivesParticipant retention is a significant challenge in ageing and dementia research. This analysis investigated (a) factors associated with retention in Insight 46, a neuroscience sub-study of the 1946 British birth cohort, and (b) clinical and cognitive changes over 2.5 years of follow-up.ResultsOf 502 participants assessed at baseline (mean[SD] age: 70.5[0.7] years), 442 returned for follow-up (mean[SD] interval: 2.5[0.3] years), representing a retention rate of 88%. Being β-amyloid positive (measures using positron emission tomography), female sex, and older age at baseline associated with lower odds of retention, while completion of neuroimaging and better cognitive performance at baseline– particularly on memory testing– related to higher odds of retention. By the time of follow-up, 14 participants were deceased, 12 of whom were female. Over follow-up, improvements were noted in certain cognitive tests (face-name test, logical memory delayed recall) with declines seen in others (mini-mental state examination, digit-symbol substitution test). Increases in self- and informant-reported cognitive complaints, cognitive disorder diagnoses, and motor abnormalities were also observed, alongside declines in blood pressure. These results have implications for the interpretation and generalisability of Insight 46 data and may be relevant to the planning of other longitudinal studies in this field.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13104-025-07323-y.

This study focuses on the cognitive diagnosis (CD) of scientific argumentation ability. A hierarchical model of six cognitive attributes was developed by expert cognitive analysis, utilizing the Toulmin Argument Model and SOLO Taxonomy theory. The research was conducted in two stages: In the first stage, a 33-item diagnostic test was developed based on Q-matrix theory, and the validity of the cognitive model and Q-matrix was verified using data from a sample of 240 tenth-grade students (M age = 15.6 years, SD = 0.72); and in the second stage, through multi-model fitting comparisons, the DINA model was identified as the optimal model, and students' scientific argumentation ability was diagnosed. The diagnostic results demonstrated that the construction of the cognitive model and Q-matrix was valid and the DINA model could effectively diagnose the cognitive structure of scientific argumentation, further constructing students' learning paths and providing empirical evidence for the targeted design of scientific argumentation thinking training. These outcomes transform general ability assessment into diagnosis of specific cognitive components, offering targeted evidence for instructional intervention. Overall, the study provides a reliable theoretical and empirical foundation for the assessment and improvement of scientific argumentation competency.

This study applied the Cognitive Diagnostic Model (CDM) to develop a personalized recommendation algorithm for rehabilitation intervention in children and adolescents with autism spectrum disorder (ASD). A total of 3,319 children and adolescents were included. Model selections recommended the Generalized Deterministic Input, Noisy "Or" Gate Model (GDINA), to simulate the response pattern of participants in the Autism Behavior Checklist. Both absolute and relative indices confirmed that the response pattern of the participants displayed acceptable fitness to GDINA. Twenty-eight symptom modalities were identified, but only 12 were assigned to over one percent of this sample. Language dysfunction is commonly observed. A diagram of the possible developmental trajectory of participants with ASD indicates that sensory and related functions can be primary targets for those with severe autistic symptoms. One possible rehabilitation route was identified in this diagram that involved 2,621 participants. A detailed personalized analysis was demonstrated in randomly selected cases from this sample. Our study developed a personalized recommended algorithm using CDM in designing individualized interventions for children and adolescents with ASD. First, our results confirmed the heterogeneity of ASD symptoms. Importantly, the information derived from the CDM allowed for the construction of a possible development diagram of the functions defined by ABC. Although these results are theoretically sound and reasonable, they remain data-driven. Further empirical validation, particularly through experience with rigorous design, is necessary to confirm the alignment between real-world practices and data-driven models.

ABSTRACT The Test of Chinese as a Heritage Language, or Huawen Shuiping Ceshi (HC), is a newly developed proficiency test by a university in Guangzhou, China. It defines “Heritage Language” as a family-transmitted language that is non-dominant in broader society and often incompletely acquired. The reading test is one of its three subtests. To investigate the cognitive patterns of learners of Chinese as a heritage language and to provide diagnostic assessment for test takers and educational institutions, the Rule Space Model (RSM) was implemented to conduct a diagnostic research of 236 test takers’ responses on the reading test (Level 3). The research results indicate that: (1) the reading attributes of Chinese as a Heritage Language (Reading) were relatively accurately determined, and the hierarchical relationships of attributes were supported by the empirical data; (2) there were 12 mastery patterns of reading attributes for learners of Chinese as a heritage language. The RSM was successfully implemented in the diagnostic assessment of the reading test. This study can enhance the understanding of Chinese reading ability, advance the application of cognitive diagnosis in Chinese reading tests, and offer more detailed insights for reading instruction.

BackgroundMounting evidence shows relationships between the cerebrovasculature, cognitive decline, neurodegeneration, and amyloid accumulation that underscores the contribution of cerebrovascular atherosclerotic disease (CA) to the pathogenesis of Alzheimer's disease and related dementias (AD/ADRD). Previously, we identified 114 brain proteins associated with CA as measured by carotid artery intimal media thickness (IMT). That finding suggests that surrogate brain biomarkers of CA may hold promise as sensitive markers for early ADRD detection. Compared to brain tissue, cerebrospinal fluid (CSF) is more accessible and can be collected from living persons. Thus, among the brain proteins profiled, we also profiled 6 (CBR1|P16152, ENDOD1|O94919, MOG|Q16653. PCSK1|P29120, PLP1|P60201, and QDPR|P09417) in CSF. In this follow‐up study, we explored the CSF proteome for associations between profiled proteins and CA as measured by carotid artery IMT. Hence, this study aims to explore potential biomarkers of CA and ADRD risk by examining associations between CSF proteins and IMT.MethodAll CSF samples and vascular measurements were collected from Emory Goizueta Alzheimer's Disease Research Center research participants. We described the CSF collection steps and multiplex proteomic steps previously.7 We performed proteomic analysis on 86 CSF samples, followed by linear regression adjusted for age, sex, and cognitive diagnosis (control vs. impaired) to test associations with CA. We applied BoxCox transformation to IMT measures to improve normality and we excluded participants with AD.ResultData for 83 people were analyzed. The sample's mean age was 65.8 (8.02); 60% were female, 70% were white adults, and 69% were controls. Among white participants, QDPR|P09417 (neurotransmitter production) was associated with higher IMT. For AA participants, PLP1|P60201 (myelin sheath) was associated with higher IMT. In meta‐analysis (both groups), QDPR|P09417 and MOG|Q16653 (myelin sheath) were associated with higher mean carotid IMT.ConclusionVascular disease can co‐occur with AD. Our results show associations between CSF proteins involved in structural integrity and chemical signaling and CA in a sample with impaired and normal cognition. Further, we detected racial differences in these associations. Given these findings among cognitively normal and impaired people, these proteins may have promise as early disease indicators. More extensive study with a larger sample is needed.

BackgroundResearch on gender and ethnic differences in white matter tracts yields mixed results. Although men show larger global corpus callosum (CC) in absolute values, women have demonstrated larger splenium and isthmus. Additionally, men have shown greater white matter and fractional anisotropy (FA) in multiple regions than women. This study investigates sex and ethnic differences in the corpus callosum and transcallosal white matter tissue microstructure of older individuals with normal cognition and memory disorders.Method191 participants (Female n = 120; Hispanics n = 115; Non‐Hispanics n = 76; Age mean = 71.07; SD = 7.75) were included in the sample from the 1Florida ADRC with cognitively normal (n = 75), MCI (n = 88), and dementia (n = 28) diagnoses. Corpus callosum volumes were collected using a Siemens Skyra 3T MRI scanner and were divided into anterior, mid‐anterior, central, mid‐posterior, and posterior regions. Also, MRI‐based free water diffusion tensor imaging (FW‐DTI) measured FW‐corrected FA (fwcFA) to assess and compare the integrity of transcallosal tract templates (TCATT) across motor, temporal, parietal, and occipital regions across males and females and ethnic groups.ResultUnivariate ANCOVAs controlling for age and clinical diagnosis were performed; females had larger normalized CC volumes in the posterior, F(1, 184) = 8.92, p = .003, ηp2 = .046, mid‐anterior, F(1,184)= 5.68, p = .018, ηp2 = .030, and anterior, F(1,184)= 14.54, p < .001, ηp2 = .073, regions, which all survived FDR corrections, with no ethnic differences observed. For transcallosal tracts, females had higher fwcFA in the inferior temporal gyrus, ηp2 Hispanics had higher fwcFA in the premotor cortex, ηp2ηp2ConclusionSex and ethnicity influence white matter changes during aging independently of age and cognitive diagnosis. However, the effect explained by these demographic variables was very small, suggesting the influence of additional factors.

BackgroundCerebral small vessel disease (CSVD) is the most common cause of vascular cognitive impairment, but its relationship to cognition across the neurocognitive spectrum is not fully understood. CSVD burden can be inferred from several magnetic resonance imaging (MRI) markers, which can be combined to generate a CSVD score. We investigated the association between CSVD score with various cognitive measures.MethodBaseline data from 972 participants [Table 1] from the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS‐ND) study were analyzed [11.9% cognitively unimpaired [CU], 14.7% subjective cognitive decline [SCD], 36.4% mild cognitive impairment [MCI], 36.9% dementia). Brain MRI scans were visually rated for Standards for Reporting Vascular Changes on Neuroimaging (STRIVE)‐based evidence of vascular brain injury (lacunes, microbleeds, white matter hyperintensities [WMH], cortical superficial siderosis [cSS], enlarged perivascular spaces [EPVS]). Three CSVD scores corresponding to global, cerebral amyloid angiopathy (CAA‐CSVD)‐specific, and hypertensive arteriopathy (HTNA‐CSVD)‐specific CSVD burden were generated [Table 2]. Cognitive measures included the Montreal Cognitive Assessment (MoCA), Clinical Dementia Rating sum of boxes (CDR‐SB), and a composite neuropsychological battery test z‐score. We modelled CSVD score (exposure) associations with five outcomes: Hachinski ischemic score (negative binomial regression), MoCA total score (linear regression), CDR‐SB (median quantile regression), and neuropsychological battery composite z‐score (linear regression), cognitive diagnosis (ordinal logistic regression). Covariates included age, sex, and education.ResultGlobal, CAA‐ and HTNA‐CSVD scores were all associated with greater Hachinski ischemic score, poorer MoCA score, higher CDR‐SB, and poorer composite neuropsychological battery test z‐score [Table 3]. Both global CSVD score (adjusted odds ratio [aOR]=1.14, 95%CI: [1.02, 1.26], p = .02) and HTNA‐CSVD score (aOR=1.18, 95%CI: [1.04, 1.35], p = .01) were associated with higher odds of a more severe cognitive diagnosis compared to a less severe cognitive diagnosis (e.g., dementia vs MCI/SCD/CU), but not CAA‐CSVD score (aOR=1.09, 95%CI: [0.97, 1.24], p = .15).ConclusionOlder adults with greater CSVD burden, as evidenced by multiple MRI markers, exhibit poorer cognition and functional performance across the neurocognitive continuum. Notably, these associations were observed not only for global CSVD but also for both CAA‐ and HTNA‐specific CSVD scores, suggesting the importance of evaluating these specific pathologies when assessing cerebrovascular contributions to cognitive decline.

Life expectancy is increasing globally. A substantial expected rise in older age groups in the population is reflected in prisons, but focus on elderly offenders remains limited compared to other age groups. This is of concern, as cognitive impairments are often present among elderly offenders, possibly affecting their behaviour, criminal responsibility and responsiveness to treatment. The objective of this study was to explore the associations between various cognitive variables, neurological diagnosis and different types of crime by offenders of 60years or older. In this retrospective records-based clinical study, we used the reports about adult defendants who underwent a full forensic psychiatric evaluation at the request of the court to assess criminal responsibility for serious crime. Individuals aged 60 and over were included, resulting in a sample of 81 persons, just seven of them women. A homogeneity analysis (HOMALS) was conducted to examine the possibility of classifying such people according to cognitive and offence status. Nearly two-thirds of these people under criminal charges had some evidence of cognitive impairment. Three main neurocognitive groups were apparent: those with clear cognitive deficiencies, and usually a clinical diagnosis of dementia, those with some cognitive impairment and those with equivocal or not cognitive impairment. The first group was older and most likely to have violence charge(s), the second to be characterised by both violent and sexual charge(s) and the third more various offending, albeit including violence. This first study of detailed neurocognitive assessment of older people under trial for a serious criminal offence indicates that it is important for older people charged with offences to have sound cognitive assessment, in their interests, and for planning effective management of risk of any further offending. Future studies should investigate relationships between cognitive problems and type of crime in a larger and perhaps more varied group.

BackgroundAlzheimer's disease (AD) diagnosis has shifted from a purely clinical framework to a clinical-biological paradigm, driven by biomarker integration. This evolution is motivated by the wider availability of reliable biomarkers and the advent of disease-modifying treatments.ObjectiveTo assess changes over time in clinical characteristics, diagnostic pathways, and healthcare resource utilization in a real-world cohort of individuals with cognitive impairment attending a Memory Clinic.MethodsThis secondary data retrospective observational study analyzed two patient cohorts with newly diagnosed cognitive impairment: one from 2017-2019 and another from 2021-2023. Anonymized medical records and structured hospital data were examined using natural language processing to extract demographic and clinical information, diagnostic pathways, treatment patterns and comorbidities.ResultsThe 2021-2023 cohort was significantly younger, exhibited higher baseline Mini-Mental State Examination scores, and underwent more instrumental assessments than the 2017-2019 cohort. These findings likely reflect a shift in public awareness and attitudes toward cognitive health. AD diagnoses increased in both cohorts over time, while mild cognitive impairment diagnoses declined. The use of diagnostic combinations was more frequent in the recent cohort, in which clinical-biological diagnoses were significantly more prevalent.ConclusionsThis study provides real-world insights into the evolving landscape of cognitive impairment diagnostics and care, underscoring a shift toward earlier, biologically grounded diagnosis, supporting precision medicine in AD care. The expanded use of biomarkers reflects evolving practice standards and prepares the ground for disease-modifying therapies in AD.

ABSTRACT Cognitive decline of elderly is significant, and early diagnosis is the key to improving long term outcomes. The existing approaches to the cognitive impairment diagnosis are usually costly, time-consuming, and are mainly based on clinical tests, which may overlook the early indicators. In the work, we suggest using smart watches, more precisely heart rate variability (HRV), physical activity and sleep patterns, and machine learning algorithms to predict cognitive decline. Our model Support Vector Machine (SVM) has good accuracy with 98.21% accuracy, 98.21% precision and 98.21% recall. The low rates of errors are together shown by the method having low false positive rates and false negative rates. Such advances demonstrate the success of wearable technology when it comes to monitoring health on a constant basis and non-invasively. This work contributes to the development of an early method of impairment detection, which is easily accessible and used in real-time, and will greatly improve tailored care of the elderly group.

Towards cost-effective cognitive impairment diagnosis systems by emulating doctors' reasoning with deep reinforcement learning.

BackgroundWhile intrusions in verbal memory tests are known to indicate a cognitive decline in aging, their role in verbal fluency (VF) tasks remains underexplored. Aims: to determine if intrusion percentages in phonemic (PF) and category (CF) VF tasks differ across cognitive diagnoses, longitudinally (across 3 years), and if they are associated with frontal lobe, temporal lobe, and hippocampal volumes.MethodsParticipants were from the 1Florida Alzheimer's Disease Research Center (ADRC), including 102 CN, 160 MCI, and 34 dementia participants at year 1 (M (age) = 72.17; 62.66% female; Table 1), with year 2 and year 3 follow‐ups. The Clinical Dementia Rating (CDR‐GS) global score was used to determine cognitive status, (i.e. normal [CDR‐GS = 0], MCI [CDR‐GS = 0.5], or dementia [CDR‐GS = 1 ≥1]). Intrusion percentages from PF and CF tasks (collected every 15.36 months, on average) were analyzed alongside brain volumes (one‐time MRI scans collected at baseline).ResultsTwo separate MANCOVAs revealed intrusion percentages on VF tasks at baseline (year 1) significantly differed across year 2 diagnostic groups, as well as year 3 diagnostic groups (Figure 1), after controlling for demographic variables. This indicated intrusion percentages were significantly associated with diagnostic outcomes at year 2 and at year 3. Individuals with dementia exhibited higher intrusion percentages than cognitively normal individuals. Negative correlations were found between baseline VF intrusion percentages and brain volumes in frontal (left frontal pole and CF: r (296) = ‐.161, p = .005; left rostral midfrontal & CF: r (296) = ‐.151, p = .009; left caudal midfrontal & PF: r (285) = ‐.151, p = .010; right frontal pole & CF: r (296) = ‐.159, p = .006) and temporal regions (left inferior & CF: r (296) = ‐.158, p = .006: right middle and PF: r (296) = ‐.157, p = .008), after Bonferroni corrections. Regression analyses revealed higher baseline VF intrusion percentages were significantly associated with reduced left frontal pole and left caudal mid‐frontal volumes (Table 2).ConclusionIntrusion percentages in VF tasks may be a sensitive marker of cognitive decline in aging and structural changes in the left frontal lobe.

BackgroundPrimary care providers (PCPs) are at the forefront of evaluating cognitive concerns and detecting mild cognitive impairment and dementia, but they generally lack training and tools to do so.MethodAn intervention consisting of education webinars integrated with checklists in the electronic health record (EHR) and a set of exam room tools was developed and implemented across a large primary care system of 14 community‐based clinics (94 PCPs). Outcomes from the EHR included the number of cognitive assessments recorded by PCPs in the EHR and the number of patients who received a new diagnosis of mild cognitive impairment or dementia.ResultOver two years of the program, the average number of cognitive assessments entered by quarter into the EHR increased from 6.6 to 42.8 (p = 0.01). In addition, the average number of new diagnoses of mild cognitive impairment or dementia per quarter increased from 17.0 to 37.8 (p = 0.02). See Figures 1 and 2. Referrals to specialty care were reported as being more useful, because they more often included assessments of cognitive function and a review of potential reversible causes of cognitive impairment.ConclusionAn intervention integrating PCP education with workflow tools increased cognitive testing and diagnoses of mild cognitive impairment and dementia in a large primary care health system. Such change is essential for patients to receive improved care for Alzheimer's disease and related dementias.

BackgroundSubtle behavioral changes may signal early cognitive decline, presenting opportunities for intervention. Mild Behavioral Impairment (MBI) is recognized as a precursor to cognitive decline and Alzheimer's disease (AD). The MBI‐Checklist (MBI‐C) has been validated as an effective tool for detecting MBI and assessing neuropsychiatric symptoms. However, its predictive value remains underexplored in Southeast Asian populations. Cultural factors may contribute to underreporting of emotional and behavioral symptoms, making objective assessment crucial. This study evaluates whether MBI‐C scores, assessed longitudinally, predict cognitive outcomes.MethodThe study included 571 participants (mean age:61.43±9.82years;44%male) from the Biomarkers and Cognition Study, Singapore (BIOCIS), assessed over two visits, one year apart. Participants were classified as cognitively normal, subjective cognitive decline, mild cognitive impairment, or mild dementia based on established criteria. Behavioral symptoms were measured using the self‐reported MBI‐C with a clinical cutoff of 5.5. Longitudinal cognitive outcomes were analyzed using linear mixed‐effects models, incorporating MBI‐C total and subdomain scores at Visit 1 as predictors. Covariates included age, gender, education years, and APOEε4 status. A Visit×MBI‐C interaction term assessed temporal effects. Random intercepts captured individual variability.ResultAt Visit 1, participants with clinical MBI‐C scores demonstrated significantly poorer performance in global cognition (p = .035), verbal episodic memory (p = .020), verbal and associative episodic memory (p = .005 for immediate‐recall, p = .010 for delayed‐recall), visuospatial memory (p = .001 for immediate‐recall, p = .019 for delayed‐recall), and attention (p = .010). Higher MBI‐C total scores at Visit 1 significantly predicted worse cognitive diagnoses at Visit 2(β=0.163, p < .001). The Visit×MBI‐C interaction (β=0.163, p < .001) suggested an intensifying relationship between MBI‐C scores and cognitive outcomes over time. Subdomain analyses revealed significant associations between cognitive decline and MBI‐C domains, including Interest (β=0.266, p < .001), Mood (β=0.193, p = .001), Social (β=0.367, p = .012), Abnormal Beliefs (β=0.447, p = .015), and Impulse Control (β=0.196, p < .001).ConclusionSelf‐reported MBI‐C scores predict early cognitive decline, reinforcing MBI symptoms as clinical markers. The strengthening association over time underscores their progressive impact on cognition. Notably, the Abnormal Beliefs subdomain showed the strongest association with cognitive deterioration, suggesting its utility as a marker for progression. Addressing MBI could mitigate cognitive decline, emphasizing its role in early intervention. Furthermore, the tendency in Southeast Asia to underreport behavioral symptoms may indicate that observed associations reflect more severe underlying pathology, reinforcing the robustness of these findings.

Cognitive Diagnosis Modeling is a fundamental task in intelligent education, intending to assess students’ mastery levels on knowledge concepts through interactions. Previous methodologies prioritized enhancing average diagnostic accuracy. However, they often neglected the Long-Tailed issue in interactions. To address this issue, we propose an A daptive S elf-supervised Graph Learning C ognitive D iagnosis (ASCD) framework that leverages relatively balanced sparse views to forcing the graph network to focus on long-tailed nodes, aiming to tackle the long-tailed problem in graph-based cognitive diagnosis. Additionally, we employ self-supervised manners to mitigate the impact of dropped information on other nodes. Our approach leverages the adaptive graph confusion method to create sparse views of the original student-exercise interaction graph. In these sparse views, both long-tailed and head students carry similar weights, pushing the graph network to allocate attention impartially across all students. We integrate two different graph confusion techniques into adaptive graph confusion to accommodate varying degrees of data sparsity: edge dropout and feature masking. ASCD can serve as a plug-and-play module integrated into any graph-based cognitive diagnosis model, enhancing its performance regarding long-tailed scenarios. Extensive experiments on real-world datasets show the effectiveness of our approach, especially on the students with much sparser interaction records.

Abstract The multiple-choice (MC) item format has been adapted to the cognitive diagnosis (CD) framework. Early approaches simply dichotomized the responses and analyzed them with a CD model for binary responses. Obviously, this strategy cannot exploit the additional diagnostic information provided by MC items. De la Torre’s (2009, Applied Psychological Measurement , 33, 163–183) MC-DINA model was the first for the explicit analysis of MC items. However, the q-vectors of the distractors were constrained to be nested within the key and each other, which imposes serious restrictions on item development. Relaxing the nestedness-constraint, comes at a price. First, distractors may become redundant: they do not improve the classification of examinees beyond the response options already available for an item. Second, undesirable diagnostic ambiguity can arise from distractors that are equally likely to be chosen by an examinee, but have distinct attribute profiles pointing at different diagnostic classifications. In this article, two criteria, plausible and proper , are developed for detecting these problematic cases. Two theorems that permit for the detection and amendment of improper and implausible items are presented. An R function serving this purpose is used in several practical applications. Results of simulation studies and real data analysis are also reported.

BackgroundElevated blood pressure (BP) is considered a risk factor for cognitive disorders. This study examined the continuous relationship between 2-year mean systolic BP (SBP) and incident cognitive disorders in a large, real-world primary care cohort.ObjectiveTo evaluate the associations of 2-year mean SBP with incident cognitive impairment in a 'real-world' clinical observational dataset.MethodsWe conducted a longitudinal analysis of electronic health records (EHR) from primary care patients aged 50 or older. Patients were included if they had ≥2 SBP measurements and no cognitive disorder diagnosis prior to or within 3 years of their first included SBP measurement. Exposure was the average SBP in the two years following the first measurement. We estimated associations between (continuous) average SBP and incident cognitive impairment using a Cox model. Records from 160,052 patients were included in the analysis.ResultsIn our study, SBP ≥ 140 mmHg is not associated with a statistically significant difference in cognitive disorder risk compared to lower SBP (<120 and 120-140 mmHg). In the ages 60-69 group, SBP 135 mmHg is the lowest SBP at which a significantly decreased risk for cognitive disorders was observed (HR, 0.84; 95% CI, 0.82-0.89; p < 0.05). Among unmedicated individuals, 136 mmHg SBP was the lowest SBP at which a statistically significant association with decreased cognitive disorder risk was observed (HR, 0.92; 95% CI, 0.87-1.00; p < 0.05).ConclusionsThe association between SBP and cognitive disorder risk varies by age and BP medication use. This highlights the need for individualized BP targets to mitigate cognitive impairment.

As older adults live longer in the community, many, particularly those with dementia, may become bedbound at the end of life and require extensive support from caregivers. To estimate the prevalence and associated characteristics of bedbound status during the last year of life among community-dwelling decedents, and to examine differences by dementia status. This cross-sectional study used data from rounds 1 through 13 from the National Health and Aging Trends Study to identify individuals aged 65 years or older who were community-dwelling 12 months before death between the years 2011 and 2023. Data were analyzed between December 2024 and August 2025. The primary outcome was bedbound status, defined by self or proxy report incorporating frequency, help required, and level of difficulty leaving bed and/or bedroom. Probable dementia was defined using an algorithm that includes cognitive testing and diagnosis history. The number of months between each participant's date of death and their last interview prior to death was calculated. Bedbound prevalence was estimated by dementia status in monthly intervals up to 1 year before death, along with the amount of caregiver help received. Survey-weighted multivariable regression was used to estimate the likelihood of being bedbound during the last year of life by sociodemographic, clinical, and health characteristics. Among 3168 decedents (mean [SD] age, 83.0 [0.2] years, 51.9% female; 36.1% [95% CI, 34.7%-39.0%] with dementia), 16.6% (95% CI, 15.0%-18.3%) were bedbound during their last year of life, representing nearly 2.6 million decedents. As individuals with dementia neared death, bedbound prevalence increased from 28.6% (95% CI, 13.9%-47.4%) in the 12 months prior to death to 77% (95% CI, 56.4%-91.6%) in the last month of life. Probable dementia was associated with nearly 5 times greater odds of being bedbound in the last year of life compared with having possible or no dementia (odds ratio, 4.58; 95% CI, 3.09-6.79; P < .001). Bedbound decedents received nearly 3 times as many hours of care per week compared with individuals who were not bedbound (98.00 [95% CI, 88.86-107.14] vs 34.03 [95% CI, 31.74-36.32] h/wk; P < .001). In this cross-sectional study of decedents, being bedbound during the last year of life was common among individuals with dementia living in the community and imposed substantial demand on caregivers. These findings underscore the urgent need for policies that ensure access to comprehensive home-based care and services to support patients and families.

The Q-matrix of a cognitively diagnostic assessment (CDA), documenting the item-attribute associations, is a key component of any CDA. However, the true Q-matrix underlying a CDA is never known and must be estimated-typically by content experts. However, due to fallible human judgment, misspecifications of the Q-matrix may occur, resulting in the misclassification of examinees. In response to this challenge, algorithms have been developed to estimate the Q-matrix from item responses. Some algorithms impose identifiability conditions while others do not. The debate about which is "right" is ongoing; especially, since these conditions are sufficient but not necessary, which means viable alternative Q-matrix estimates may be ignored. In this study, the performance of Q-matrix estimation algorithms that impose identifiability conditions on the Q-matrix estimate was compared with that of estimation algorithms which do not impose such identifiability conditions. Large-scale simulations examined the impact of factors like sample size, test length, attributes, or error levels. The estimated Q-matrices were evaluated for meeting identifiability conditions and their accuracy in classifying examinees. The simulation results showed that for the various estimation algorithms studied here, imposing identifiability conditions on Q-matrix estimation did not change outcomes with respect to identifiability or examinee classification.

IntroductionThe global rise in the population aged over 65 has led to a corresponding increase in cognitive impairment diagnoses, with dementia as a predominant condition characterised by diverse aetiopathogenetic profiles. Behavioural and psychological symptoms of dementia (BPSD) encompass a range of psychiatric, behavioural and cognitive symptoms associated with cognitive impairment. BPSD significantly affects patients, caregivers and healthcare providers, often necessitating interventions like sedatives or physical restraints that may worsen patient outcomes. Emerging evidence underscores the need for innovative, non-pharmacological interventions to manage BPSD effectively.The current study intends to investigate the feasibility and acceptability of customised, immersive virtual reality environments (iVRe) to reduce responsive behaviours among individuals with dementia. Building on prior findings demonstrating virtual reality (VR) potential in reducing anxiety and fostering positive emotional states, this pilot study assesses the feasibility, safety and user engagement of customised iVRe interventions.Methods and analysisA longitudinal, mixed-methods design will be employed, enrolling 20 elderly participants with varying levels of cognitive impairment from the APSP ‘Margherita Grazioli’ long-term care facility in Trento. Participants undergo three VR exposure sessions featuring tailored environments adjusted in real-time for visual and auditory preferences. Data collection integrates standardised self-report questionnaires, observational tools and clinical records. Measures include the Functional Assessment Staging Tool, Neuropsychiatric Inventory and Cohen-Mansfield Agitation Inventory, as well as tools assessing pain, anxiety and emotional states before, during and after VR sessions.Ethics and disseminationThe study protocol has been approved by the Comitato Etico Territoriale della Provincia Autonoma di Trento per le Sperimentazioni Cliniche, Azienda Provinciale per i Servizi Sanitari—Trento, Italy (Rep. Int. 12090, 15 May 2025). All participants or their legal representatives will provide written informed consent prior to enrolment. Deidentified data will be securely stored on institutional servers at the Fondazione Bruno Kessler and the APSP ‘Margherita Grazioli’, curated in compliance with the General Data Protection Regulation, and retained for 3 years after study completion. Any data shared externally will be provided in fully anonymised form, and only for scientific purposes, subject to prior ethical and legal approval. Study findings will be disseminated through peer-reviewed publications, conference presentations and executive summaries shared with participating institutions and stakeholders.Trial registration numberNCT06693193.