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Community-dwelling people with Mild Cognitive Impairment self-reporting not to be meeting recommended physical activity levels participated in this study to (a) determine compliance of wearing (thigh-worn) accelerometers, (b) describe physical activity levels and sedentary behavior, and (c) determine the validity of the Physical Activity Scale for the Elderly (PASE) compared with activPAL accelerometers. A total of 79 people had valid accelerometer data (median [interquartile range]: age, 71 [54–75] years). Compliance was 86.81%. Participants were sedentary for 10.6 hr per day and engaged in a median of 9 min per day of moderate-intensity physical activity. Fair correlations were found between the PASE and total stepping time per day (r =.35, p <.01), total number of steps per day (r =.36, p <.01), and number of steps in stepping activities completed for ≤1 min (r =.42, p <.01). The PASE and Standing time (r =.04, p =.724) and PASE and Sitting time (r =.04, p =.699) had little to no relationship. The use of thigh-worn accelerometers for this population is achievable. People with Mild Cognitive Impairment have high levels of sedentary behavior and minimal engagement in moderate-intensity physical activity. The PASE has fair, positive criterion validity with activity-based outcomes measured by activPAL accelerometers but not with sedentary behavior, which is high for this population. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairments are common in alcohol use disorder (AUD), but only a few studies have investigated the accuracy of the Montreal Cognitive Assessment (MoCA) in this population. We examined the accuracy and precision of the MoCA in detecting cognitive impairment in a sample of patients with AUD. In addition, we investigated whether the MoCA predicts premature discontinuation from treatment. Method: A sample of 126 persons with AUD undergoing treatment in specialist health services were administered the MoCA and a battery of 12 neuropsychological tests. Five cognitive domains were derived from the reference tests. A composite total score from these tests was used as a reference criterion for determining correct and incorrect classifications for the MoCA. We analyzed the optimal cut‐off score for the MoCA and the accuracy and agreement of classification between the MoCA and the reference tests. Results: Receiver operating characteristic (ROC) curve analyzes yielded an area under the curve (AUC) of 0.77 (95% CI [0.67, 0.87]). Applying 25 as the cut‐off, MoCA sensitivity was 0.77 and specificity 0.62. The PPV was 0.53. The NPV was 0.84. Using a cut‐off score of 24 yielded a lower sensitivity 0.60, but specificity was significantly better i.e., 0.79. PPV was 0.68. The NPV was 0.82. Kappa agreement between MoCA and the reference tests was fair to moderate, 0.38 for the cut‐off of 25, and 0.44 for the cut‐off of 24. MoCA did not predict discontinuation from treatment. Conclusions: Our findings indicate limitations in the classification accuracy of the MoCA in predicting cognitive impairment in AUD. Achieving the right balance between accurately identifying impaired cases without including too many false positives can be challenging. Further, MoCA does not predict discontinuation from treatment. Overall, the results do not support MoCA as a time‐efficient screening instrument. [ABSTRACT FROM AUTHOR]

The article presents a study which explored the relationship between cognitive function and oral health in home-dwelling Norwegian older adult population. Topics discussed include examinations conducted among participants of the study, assessment of the relationships between cognitive function and the number of decayed teeth, and estimate made on the relationship between cognitive function and the prevalence of severe periodontitis.

Background: Delirium is the most common psychiatric diagnosis in the intensive care unit (ICU), and 55%‐80% of delirium cases are unrecognized and undocumented the most popular validated instruments available to diagnose delirium for critically ill patients are the Confusion Assessment Method for the ICU (CAM‐ICU). [Correction added on 16 October 2024, after first online publication: The Background section in Abstract has been added in this version.] Aim: To identify the validity and predictability of the confusion assessment method for the intensive care unit (CAM‐ICU) for delirium in critically ill patients in the ICU. Study Design: In this systematic review, PubMed, Embase, Cochrane Central Register of Controlled Trials, and MEDLINE databases were searched for observational studies investigating delirium screening tools for ICU patients. In the meta‐analysis, we combined the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve (AUC) of SROC to analysis the predictive value of CAM‐ICU. Results: Twenty‐nine articles met the inclusion criteria. The pooled sensitivity and specificity values were 0.82 (95% confidence interval [CI]: 0.75–0.87) and 0.95 (95% CI: 0.93–0.97), respectively. The AUC point estimate of the SROC curve was 0.96 (95% CI: 0.94–0.97). Race (Asian or Others) could affect the pooled sensitivity and specificity, and the analysis method (Patient‐ or Scan‐based) and study design were not sources of heterogeneity for pooled sensitivity and specificity. Conclusions: The CAM‐ICU is a valid and reliable tool for delirium prediction among ICU patients. When introducing CAM‐ICU to assess delirium, it is necessary to localize its language and content to improve its predictive efficacy in different countries and different ethnic groups. Relevance to Clinical Practice: In clinical practice, nurses can use CAM‐ICU to evaluate delirium in critically ill patients in ICU. However, it is necessary to debug the language and content according to the application population. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the effectiveness of multimodal features based on gait analysis and eye tracking for elderly people screening with subjective cognitive decline in the community. Methods: In the study, 412 cognitively normal older adults aged over 65  years were included. Among them, 230 individuals were diagnosed with non-subjective cognitive decline and 182 with subjective cognitive decline. All participants underwent assessments using three screening tools: the traditional SCD9 scale, gait analysis, and eye tracking. The gait analysis involved three tasks: the single task, the counting backwards dual task, and the naming animals dual task. Eye tracking included six paradigms: smooth pursuit, median fixation, lateral fixation, overlap saccade, gap saccade, and anti-saccade tasks. Using the XGBoost machine learning algorithm, several models were developed based on gait analysis and eye tracking to classify subjective cognitive decline. Results: A total of 161 gait and eye-tracking features were measured. 22 parameters, including 9 gait and 13 eye-tracking features, showed significant differences between the two groups (p  <  0.05). The top three eye-tracking paradigms were anti-saccade, gap saccade, and median fixation, with AUCs of 0.911, 0.904, and 0.891, respectively. The gait analysis features had an AUC of 0.862, indicating better discriminatory efficacy compared to the SCD9 scale, which had an AUC of 0.762. The model based on single and dual task gait, antisaccade, gap saccade, and median fixation achieved the best efficacy in SCD screening (AUC  =  0.969). Conclusion: The gait analysis, eye-tracking multimodal assessment tool is an objective and accurate screening method that showed better detection of subjective cognitive decline. This finding provides another option for early identification of subjective cognitive decline in the community. [ABSTRACT FROM AUTHOR]

Introduction: Research has shown that speech analysis demonstrates sensitivity in detecting early Alzheimer's disease (AD), but the relation between linguistic features and cognitive tests or biomarkers remains unclear. This study aimed to investigate how linguistic features help identify cognitive impairments in patients in the early stages of AD. Method: This study analyzed connected speech from 80 participants and categorized the participants into early-AD and normal control (NC) groups. The participants underwent amyloid-ß positron emission tomography scans, brain magnetic resonance imaging, and comprehensive neuropsychological testing. Participants' speech data from a picture description task were examined. A total of 15 linguistic features were analyzed to classify groups and predict cognitive performance. Results: We found notable linguistic differences between the early-AD and NC groups in lexical diversity, syntactic complexity, and language disfluency. Using machine learning classifiers (SVM, KNN, and RF), we achieved up to 88% accuracy in distinguishing early-AD patients from normal controls, with mean length of utterance (MLU) and long pauses ratio (LPR) serving as core linguistic indicators. Moreover, the integration of linguistic indicators with biomarkers significantly improved predictive accuracy for AD. Regression analysis also highlighted crucial linguistic features, such as MLU, LPR, Type-to-Token ratio (TTR), and passive construction ratio (PCR), which were sensitive to changes in cognitive function. Conclusion: Findings support the efficacy of linguistic analysis as a screening tool for the early detection of AD and the assessment of subtle cognitive decline. Integrating linguistic features with biomarkers significantly improved diagnostic accuracy. [ABSTRACT FROM AUTHOR]

The article discusses a research project focused on detecting early cognitive flexibility deficits in aging using virtual reality (VR) and artificial intelligence (AI). It highlights the importance of cognitive flexibility in daily functioning and the decline that often accompanies aging. It mentions that traditional assessment tools are criticized for their lack of ecological validity, prompting the development of a novel VR-based assessment.

BACKGROUNG: Mild traumatic brain injury (mTBI) encompasses a spectrum of disability including early cognitive impairment (ECI). The Brain Injury Guidelines suggest that patients with mTBI can be safely discharged from the emergency department. Although half of patients with mTBI with intracranial hemorrhage (ICH) have evidence of ECI, it is unclear what percentage of these patients' ECI persists after discharge. We hypothesize a significant proportion of trauma patients with mTBI and ECI at presentation have persistent ECI at 30-day follow-up. STUDY DESIGN: A single-center prospective cohort study including adult trauma patients with ICH or skull fracture and a Glasgow Coma Scale of 13 to 15 on arrival was performed. Participants were screened for ECI using the Rancho Los Amigos Scale (RLA), and ECI was defined as an RLA less than 8. We compared ECI and non-ECI groups for demographics, injury profile, CT imaging (eg Rotterdam CT score) and outcomes with bivariate analysis. Thirty-day follow-up telephone calls were performed to re-evaluate RLA for persistent ECI and concussion symptoms. RESULTS: From 62 patients with ICH or skull fracture and mTBI, 21 (33.9%) had ECI. Patients with ECI had a higher incidence of subarachnoid hemorrhage (85.7% vs 46.3%, p = 0.003) and higher Rotterdam CT score (p = 0.004) compared with those without ECI. On 30-day follow-up, 6 of 21 (26.6%) patients had persistent ECI. In addition, 7 (33.3%) patients had continued concussion symptoms. CONCLUSIONS: More than one-third of mTBI patients with ICH had ECI. At 30-day postdischarge follow-up, more than one-fourth of these patients had persistent ECI and 33% had concussion symptoms. This highlights the importance of identifying ECI before discharge as a significant portion may have ongoing difficulties reintegrating into work and society. [ABSTRACT FROM AUTHOR]

The article discusses research which aimed to quantify the daily care hours that informal caregivers provide to patients with diagnosed dementia, undiagnosed cognitive impairment (CI), and intact cognition. Cited are the characteristics of patients and caregivers included in the study, the substantial caregiving needs of older adults with undiagnosed CI, and findings highlighting the importance of cognitive screening in the emergency department (ED) and outpatient testing.

Background: Cognitive screening tools enable the detection of cognitive impairment, facilitate timely intervention, inform clinical care, and allow long‐term planning. The Montreal Cognitive Assessment for people with hearing impairment (MoCA‐H) was developed as a reliable cognitive screening tool for people with hearing loss. Using the same methodology across four languages, this study examined whether cultural or linguistic factors affect the performance of the MoCA‐H. Methods: The current study investigated the performance of the MoCA‐H across English, German, French, and Greek language groups (n = 385) controlling for demographic factors known to affect the performance of the MoCA‐H. Results: In a multiple regression model accounting for age, sex, and education, cultural–linguistic group accounted for 6.89% of variance in the total MoCA‐H score. Differences between languages in mean score of up to 2.6 points were observed. Conclusions: Cultural or linguistic factors have a clinically significant impact on the performance of the MoCA‐H such that optimal performance cut points for identification of cognitive impairment derived in English‐speaking populations are likely inappropriate for use in non‐English speaking populations. To ensure reliable identification of cognitive impairment, it is essential that locally appropriate performance cut points are established for each translation of the MoCA‐H. [ABSTRACT FROM AUTHOR]

The Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) Effort Index (EI) may be unreliable at its standard recommended cutoff score when used with forensic psychiatric inpatient populations given high rates of genuine cognitive impairment. The present study sought to (a) examine the rate of invalid performance on the RBANS EI using the standard cutoff among incompetent to stand trial (IST) inpatients and (b) investigate the psychometric properties of the RBANS EI at various cutoff scores. This study was conducted using archival assessment data collected at a large forensic psychiatric hospital. Across the entire sample (N = 238, 79% male, Mage = 45 years, 24% primary Spanish-speaking), 79% were diagnosed with a schizophrenia spectrum disorder. Experiment 1 of the study (n = 165) contained IST patients who were classified as having a valid presentation. Experiment 2 contained a valid (n = 46) and invalid (n = 27) presentation group comprised of postadjudication and IST inpatients, respectively. In Experiment 1, over one third (36.4%) of the valid presentation sample scored above the standard recommended EI cutoff. In Experiment 2, applying the standard recommended cutoff score of >3 was associated with a false positive rate of over 20%. Obtaining <10% false positive errors was achieved at a cutoff score of >5. At this cutoff score, the estimated local base rate of invalid responding among IST patients was 28%. Clinical and forensic implications and recommendations for adopting this more conservative RBANS EI cutoff score are discussed. Public Significance Statement: The current research suggests that individuals with schizophrenia spectrum disorders who reside in forensic hospital settings are at risk for being falsely identified as putting forth invalid performance on the Repeatable Battery for the Assessment of Neuropsychological Status Effort Index when using the standard recommended cutoff score, which may be related to high rates of genuine cognitive impairment. As such, an alternative Repeatable Battery for the Assessment of Neuropsychological Status Effort Index cutoff score is recommended when assessing individuals in these and similar settings. [ABSTRACT FROM AUTHOR]

Accessible SummaryWhat is known on the subject?Functioning is one of the most affected areas in schizophrenia. Social, occupational and personal domains are affected, and these deficits are responsible for a major part of the disability associated with the disorder.There are several instruments to measure functioning, but the HoNOS provides a wide assessment of impairment in 12 areas of functioning.What does the paper add to existing knowledge?The Spanish version of the HoNOS shows good properties in terms of reliability and validity for use in schizophrenia patients.Although some authors divide the scale according to proposed underlying dimensions, in schizophrenia this division may not be appropriate.What are the implications for practice?A reliable and easy‐to‐use measure of impairment in different areas of functioning is useful for optimizing the treatment and rehabilitation of patients with schizophrenia. Introduction: The HoNOS scale was designed for the assessment of psychosocial impairment in various domains. While it is widely used in psychiatric settings, it has not been validated in Spanish for use in patients with schizophrenia. Aim: To examine the psychometric properties of the Spanish version of the HoNOS scale in a sample of schizophrenia patients. Method: A total of 194 individuals aged 18 to 65 with schizophrenia spectrum diagnoses were evaluated using the HoNOS. Illness severity and level of functioning were also assessed. Results: The HoNOS showed moderate internal consistency, good inter‐observer reliability and good test‐retest reliability. Factor analysis revealed an internal structure consisting of four factors, with item distribution differing from the theoretical dimensions proposed for the original scale. Discussion: The Spanish version of the HoNOS scale is a reliable and valid instrument for assessing psychosocial impairment in individuals diagnosed with schizophrenia spectrum disorders. However, further research is needed to determine its internal structure more accurately. Implications for Practice: The HoNOS scale provides researchers and clinicians with a valid measure of impairment in twelve different domains, which can facilitate and guide the treatment of schizophrenia patients. [ABSTRACT FROM AUTHOR]

Introduction: The number of dementia patients is increasing with population aging. Preclinical detection of dementia in patients is essential for access to adequate treatment. In previous studies, dementia patients showed texture recognition difficulties. Onomatopoeia or sound symbolic words (SSW) are intuitively associated with texture impressions and are less likely to be affected by aphasia and description of material perception can be easily obtained. In this study, we aimed to create a test of texture recognition ability expressed by SSW to detect the presence of mild cognitive disorders. Methods: The sound symbolic words texture recognition test (SSWTRT) is constructed from 12 close-up photos of various materials and participants were to choose the best SSW out of 8 choices to describe surface texture in the images in Japanese. All 102 participants seen in Juntendo University Hospital from January to August 2023 had a diagnosis of possible iNPH (age mean 77.9, SD 6.7). The answers were scored on a comprehensive scale of 0 to 1. Neuropsychological assessments included MMSE, FAB, and the Rey Auditory Verbal Learning Test (RAVLT), Pegboard Test, and Stroop Test from the EUiNPH Grading Scale (GS). In study 1 the correlation between SSWTRT and the neuropsychological tests were analyzed. In study 2, participants were divided into two groups: the Normal Cognition group (Group A, n = 37) with MMSE scores of 28 points or above, and the Mild Cognitive Impairment group (Group B, n = 50) with scores ranging from 22 to 27 points, and its predictability were analyzed. Results: In study 1, the total SSWTRT score had a moderate correlation with the neuropsychological test results. In study 2, there were significant differences in the SSWTRT scores between groups A and B. ROC analysis results showed that the SSWTR test was able to predict the difference between the normal and mildly impaired cognition groups. Conclusion: The developed SSWTRT reflects the assessment results of neuropsychological tests in cognitive deterioration and was able to detect early cognitive deficits. This test not only relates to visual perception but is likely to have an association with verbal fluency and memory ability, which are frontal lobe functions. [ABSTRACT FROM AUTHOR]

Introduction: The prediction of progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is an important clinical challenge. This study aimed to identify the independent risk factors and develop a nomogram model that can predict progression from MCI to AD. Methods: Data of 141 patients with MCI were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We set a follow-up time of 72 months and defined patients as stable MCI (sMCI) or progressive MCI (pMCI) according to whether or not the progression of MCI to AD occurred. We identified and screened independent risk factors by utilizing weighted gene co-expression network analysis (WGCNA), where we obtained 14,893 genes after data preprocessing and selected the soft threshold b = 7 at an R² of 0.85 to achieve a scale-free network. A total of 14 modules were discovered, with the midnightblue module having a strong association with the prognosis of MCI. Using machine learning strategies, which included the least absolute selection and shrinkage operator and support vector machine-recursive feature elimination; and the Cox proportional-hazardsmodel, which included univariate and multivariable analyses, we identified and screened independent risk factors. Subsequently, we developed a nomogram model for predicting the progression from MCI to AD. The performance of our nomogram was evaluated by the C-index, calibration curve, and decision curve analysis (DCA). Bioinformatics analysis and immune infiltration analysis were conducted to clarify the function of early B cell factor 1 (EBF1). Results: First, the results showed that 40 differentially expressed genes (DEGs) related to the prognosis of MCI were generated by weighted gene co-expression network analysis. Second, five hub variables were obtained through the abovementioned machine learning strategies. Third, a low Montreal Cognitive Assessment (MoCA) score [hazard ratio (HR): 4.258, 95% confidence interval (CI): 1.994-9.091] and low EBF1 expression (hazard ratio: 3.454, 95% confidence interval: 1.813-6.579) were identified as the independent risk factors through the Cox proportional-hazards regression analysis. Finally, we developed a nomogrammodel including the MoCA score, EBF1, and potential confounders (age and gender). By evaluating our nomogram model and validating it in both internal and external validation sets, we demonstrated that our nomogrammodel exhibits excellent predictive performance. Through the Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes Genomes (KEGG) functional enrichment analysis, and immune infiltration analysis, we found that the role of EBF1 in MCI was closely related to B cells. Conclusion: EBF1, as a B cell-specific transcription factor, may be a key target for predicting progression from MCI to AD. Our nomogram model was able to provide personalized risk factors for the progression from MCI to AD after evaluation and validation. [ABSTRACT FROM AUTHOR]

Most clinical information is encoded as free text, not accessible for quantitative analysis. This study presents an open-source pipeline using the local large language model (LLM) "Llama 2" to extract quantitative information from clinical text and evaluates its performance in identifying features of decompensated liver cirrhosis. The LLM identified five key clinical features in a zero- and one-shot manner from 500 patient medical histories in the MIMIC IV dataset. We compared LLMs of three sizes and various prompt engineering approaches, with predictions compared against ground truth from three blinded medical experts. Our pipeline achieved high accuracy, detecting liver cirrhosis with 100% sensitivity and 96% specificity. High sensitivities and specificities were also yielded for detecting ascites (95%, 95%), confusion (76%, 94%), abdominal pain (84%, 97%), and shortness of breath (87%, 97%) using the 70 billion parameter model, which outperformed smaller versions. Our study successfully demonstrates the capability of locally deployed LLMs to extract clinical information from free text with low hardware requirements. [ABSTRACT FROM AUTHOR]

Purpose: The current study used behavioral measures of discourse complexity and story recall accuracy in an expository discourse task to distinguish older adults testing within range of cognitive impairment according to a standardized cognitive screening tool in a sample of self-reported healthy older adults. Method: Seventy-three older adults who self-identified as healthy completed an expository discourse task and neuropsychological screener. Discourse data were used to classify participants testing within range of cognitive impairment using multiple machine learning algorithms and stability analysis for identifying reliably predictive features in an effort to maximize prediction accuracy. We hypothesized that a higher rate of pronoun use and lower scores on story recall would best classify older adults testing within range of cognitive impairment. Results: The highest classification accuracy exploited a single variable in a remarkably intuitive way: using 66% story recall as a cutoff for cognitive impairment. Forcing this decision tree model to use more features or increasing its complexity did not improve accuracy. Permutation testing confirmed that the 77% accuracy and 0.18 Brier skill score achieved by the model were statistically significant (p < .00001). Conclusions: These results suggest that expository discourse tasks that place demands on executive functions, such as working memory, can be used to identify aging adults who test within range of cognitive impairment. Accurate representation of story elements in working memory is critical for coherent discourse. Our simple yet highly accurate predictive model of expository discourse provides a promising assessment for easy identification of cognitive impairment in older adults. [ABSTRACT FROM AUTHOR]

Limited research has compared cognition of people with non-central nervous system metastatic cancer (NCM) vs. metastatic brain cancer (BM). This prospective cross-sectional study was comprised 37 healthy controls (HC), 40 NCM, and 61 BM completing 10 neuropsychological tests. The NCM performed below HCs on processing speed and executive functioning tasks, while the BM group demonstrated lower performance across tests. Tasks of processing speed, verbal fluency, and verbal memory differentiated the clinical groups (BM < NCM). Nearly 20% of the NCM group was impaired on at least three neuropsychological tests whereas approximately 40% of the BM group demonstrated the same level of impairment. [ABSTRACT FROM AUTHOR]

Purpose: Impairments of hearing and auditory processing (AP) have been indicated as risk factors for dementia, but it remains unclear if persons with clinically diagnosed mild cognitive impairment (MCI) show such impairments. The objective of these analyses was to compare AP between those with and without a clinical diagnosis of MCI using a battery of AP measures. Method: Data from 274 older adults from the Keys to Staying Sharp randomized clinical trial (NCT03528486) were analyzed. A battery of AP measures in which three domains (temporal processing, binaural processing, and degraded speech understanding) were addressed by six tests was administered. Analyses were registered at https://osf.io/nga4v. Results: Those with and without a clinical diagnosis of MCI differed significantly in age, p = .002; pure-tone hearing in the left ear, p = .007; sex, p = .015; and race, p < .001. These covariates were included in multivariate analysis of covariance, which indicated significant differences between persons with and without MCI on measures of binaural processing (ps ≤ .006), but not on measures of temporal processing or degraded speech (ps ≥ .093). Pure-tone hearing averages did not significantly differ among those with or without MCI after adjusting for age, sex, and race (ps ≥ .292). Conclusions: AP in the binaural domain is impaired in MCI, but peripheral hearing did not significantly differ between those with and without MCI. Poor performance on AP measures of binaural processing may reflect problems dividing attention and may be indicative of dementia risk. Results have clinical implications for early detection of and intervention for cognitive impairment. [ABSTRACT FROM AUTHOR]

Dementia represents a significant global health challenge, with early screening during the preclinical stage being crucial for effective management. Traditional diagnostic biomarkers for Alzheimer's Disease, the most common form of dementia, are limited by cost and invasiveness. Mild cognitive impairment (MCI), a precursor to dementia, is currently identified through neuropsychological tests like the Montreal Cognitive Assessment (MoCA), which are not suitable for large-scale screening. Eye-tracking technology, capturing and quantifying eye movements related to cognitive behavior, has emerged as a promising tool for cognitive assessment. Subtle changes in eye movements could serve as early indicators of MCI. However, the interpretation of eye-tracking data is challenging. This study introduced a dementia screening tool, VR Eye-tracking Cognitive Assessment (VECA), using eye-tracking technology, machine learning, and virtual reality (VR) to offer a non-invasive, efficient alternative capable of large-scale deployment. VECA was conducted with 201 participants from Shenzhen Baoan Chronic Hospital, utilizing eye-tracking data captured via VR headsets to predict MoCA scores and classify cognitive impairment across different educational backgrounds. The support vector regression model employed demonstrated a high correlation (0.9) with MoCA scores, significantly outperforming baseline models. Furthermore, it established optimal cut-off scores for identifying cognitive impairment with notable sensitivity (88.5%) and specificity (83%). This study underscores VECA's potential as a portable, efficient tool for early dementia screening, highlighting the benefits of integrating eye-tracking technology, machine learning, and VR in cognitive health assessments. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment and dementia are frequently under-recognized. Health system strategies anchored in primary care are essential to address gaps in timely, comprehensive diagnosis. The goal of this paper is to describe the adaptation of a tablet-based brain health assessment (TabCAT-BHA) intervention and the study protocol to test its effectiveness in improving the detection of cognitive impairment, including dementia. Methods: This mixed-methods, pragmatic, cluster randomized, hybrid effectiveness-implementation trial is being conducted in two 18-month waves with 26 Kaiser Permanente Southern California primary care clinics, with 13 serving as intervention clinics and 13 as usual care clinics. Patients 65 years and older with memory concerns (n ~ 180,000) receiving care at the 26 clinics will be included in the analyses. Primary care clinics are provided the following practice supports as part of the TabCAT-BHA intervention: brief education and training on neurocognitive disorders and study workflows; digital tools to assess cognitive function and support clinician decision making and documentation; and registered nurse support during the work-up and post-diagnosis periods for primary care providers, patients, and families. The intervention was adapted based on engagement with multiple levels of clinical and operational leaders in the healthcare system. Effectiveness outcomes include rates of cognitive impairment diagnosis in primary care and rates of completed standardized cognitive assessments and specialist referrals with incident diagnoses. Implementation outcomes include acceptability-appropriateness-feasibility, adoption, and fidelity. Results: We identified seven themes organized by system-, provider-, and patient-level domains that were used to adapt the TabCAT-BHA intervention. Accordingly, changes were made to the provider education, diagnostic work-up, and post-diagnostic support. Results will be reported in fall of 2027. Conclusions: Our engagement with multiple primary and specialty care clinical and operational leaders to adapt the TabCAT-BHA intervention to these primary care clinics has informed the protocol to evaluate the intervention's effectiveness for improving the detection of cognitive impairment, including dementia, in an integrated healthcare system. Trial Registation: Clinicaltrials.gov: NCT06090578 (registered 10/24/23). [ABSTRACT FROM AUTHOR]

For half a century, Dr. Audrey Holland investigated, developed, and implemented ways to extend the assessment of adult language and cognitive-communication disorders beyond traditional impairment-based approaches. This article summarizes Dr. Holland's many groundbreaking contributions to assessment practices by describing and exemplifying major conceptual and measurement innovations that have emerged from her research of both formal and informal assessment techniques. Dr. Holland's assessment contributions encompass the development of many widely used measures of functional communication, discourse, and cognitive-communication abilities. She also contributed to the development of assessment principles that have become part of best-practice standards of care. Some of her most significant contributions include: Drawing attention to assessment within authentic functional contexts; highlighting connections between language, communication, related cognitive abilities, and broader aspects of health including quality of life; raising psychometric standards; and emphasizing the value of implementing multiple person-centered measurement techniques spanning formal and informal as well as quantitative and qualitative approaches. Dr. Holland's career-long commitment and contributions to developing more meaningful and authentic assessment practices have transformed our field and substantively elevated the quality of care and services that we are able to provide to all persons who are impacted by language and cognitive-communication disorders. [ABSTRACT FROM AUTHOR]

The article revisits the meaning of cognitive impairment in older adults using the Aging, Demographics and Memory Study (ADAMS). Topics discussed include the methods used in the study, the performance of Mini-Mental State Examination (MMSE) as a screening tool for dementia, and the clinical implications of a low MMSE score.

Background: The need for cognitive testing is increasing with the aging population and the advent of new Alzheimer disease therapies. To respond to the increased demand, the XpressO was developed as a self‐administered digital cognitive prescreening tool that will help distinguish between populations of subjective and objective cognitive impairment according to the Montreal Cognitive Assessment (MoCA). Methods: This is a prospective validation study. XpressO is composed of tasks that assess memory and executive functions. It is validated compared to the digital MoCA as a gold standard. Out of 118 participants screened from the MoCA Clinic and a family practice clinic, 88 met inclusion criteria, two participants had missing data due to incomplete tasks, 86 participants were included in the analysis; the mean age was 70.34 years. A logistic regression model was built, and its accuracy was evaluated by the sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic. Results: Analysis showed strong correlation between (1) XpressO memory tasks scores and the MoCA Memory Index Score (p‐values < 0.001), and between (2) XpressO sub‐test scores and MoCA total score (p‐values < 0.005). The AUC for predicting MoCA performance is 0.845. To classify individuals with normal and abnormal MoCA scores, two threshold values were introduced for the total XpressO scores: sensitivity of 91% at a cutoff of 72, specificity of 90% at a cutoff of 42, and an undetermined range in between. Conclusion: XpressO demonstrated high AUC, high sensitivity and specificity to predict cognitive performance compared to the digital MoCA. It may provide efficient cognitive prescreening by identifying individuals who would benefit from further clinical assessments, potentially reducing waiting times and high burden on healthcare clinics. [ABSTRACT FROM AUTHOR]

BACKGROUND: Postoperative delirium in patients receiving head and neck surgery is a pressing concern, affecting morbidity, mortality, and healthcare costs. With an aging population and about 65,000 new head and neck cancer diagnoses per year in the United States, surgery remains a primary treatment modality, regardless of age. OBJECTIVES: This article offers an educational overview of postoperative delirium management in patients receiving head and neck surgery, summarizing incidence, etiology, pathophysiology, diagnostic tests, treatment, complications, and risk factors. METHODS: A narrative literature review of articles published in the past 10 years was conducted to consolidate information on postoperative delirium in patients receiving head and neck surgery. FINDINGS: Postoperative delirium is characterized by acute, fluctuating cognitive dysfunction within 30 days after surgery, with incidence ranging from 11% to 26%. Pathophysiology is multifactorial, and pharmacologic options are limited because of side effects and varying effectiveness. Nonpharmacologic management focuses on addressing underlying causes and early mobilization. Reducing postoperative delirium risk necessitates interprofessional, systemwide collaboration. [ABSTRACT FROM AUTHOR]

Symptomatic HIV-associated neurocognitive disorder (HAND) is a complication of HIV (cognitive impairment, difficulties with everyday functioning). If detected early, interventions assist with optimizing care, avoiding rapid decline and enhancing coping. There remains inconsistency surrounding screening/diagnosis information within Australian healthcare professionals and community settings. A scoping review of academic literature, government policies and non-government organisations (NGOs) was conducted to map existing screening/diagnosis information using the guidelines of Joanna Briggs Institute. A literature search of EBSCOhost and Medline (dates: 2015–2021), the Australian government NGO web domains, Google and unpublished academic works was conducted (July 2021) and updated (December 2022) to identify Australian items (past 5 years). Seventeen items met the inclusion criteria. No government guidelines were identified. Various HIV-related organisations proposed different diagnostic guidelines. Most HAND research originated in Sydney. The most accessible information was from Dementia Australia, with some inaccuracies noted. There is scant Australian research/information on HAND screening/diagnosis. HAND translational research and screening/diagnosis standards are urgently needed to inform best practices. The Australian context is used to discuss international implications regarding higher-income countries with similar patterns/healthcare. [ABSTRACT FROM AUTHOR]

Objective: Early identification of cognitive impairment in older adults could reduce the burden of age-related disabilities. Gait parameters are associated with and predictive of cognitive decline. Although a variety of sensors and machine learning analysis methods have been used in cognitive studies, a deep optimized machine vision-based method for analyzing gait to identify cognitive decline is needed. Methods: This study used a walking footage dataset of 158 adults named West China Hospital Elderly Gait, which was labelled by performance on the Short Portable Mental Status Questionnaire. We proposed a novel recognition network, Deep Optimized GaitPart (DO-GaitPart), based on silhouette and skeleton gait images. Three improvements were applied: short-term temporal template generator (STTG) in the template generation stage to decrease computational cost and minimize loss of temporal information; depth-wise spatial feature extractor (DSFE) to extract both global and local fine-grained spatial features from gait images; and multi-scale temporal aggregation (MTA), a temporal modeling method based on attention mechanism, to improve the distinguishability of gait patterns. Results: An ablation test showed that each component of DO-GaitPart was essential. DO-GaitPart excels in backpack walking scene on CASIA-B dataset, outperforming comparison methods, which were GaitSet, GaitPart, MT3D, 3D Local, TransGait, CSTL, GLN, GaitGL and SMPLGait on Gait3D dataset. The proposed machine vision gait feature identification method achieved a receiver operating characteristic/area under the curve (ROCAUC) of 0.876 (0.852-0.900) on the cognitive state classification task. Conclusion: The proposed method performed well identifying cognitive decline from the gait video datasets, making it a prospective prototype tool in cognitive assessment. [ABSTRACT FROM AUTHOR]

Background: Vascular cognitive impairment (VCI) manifests in memory impairment, mental slowness, executive dysfunction, behavioral changes, and visuospatial abnormalities, significantly compromising the quality of daily life for patients and causing inconvenience to caregivers. Neuroimaging serves as a crucial approach to evaluating the extent, location, and type of vascular lesions in patients suspected of VCI. Nevertheless, there is still a lack of comprehensive bibliometric analysis to discern the research status and emerging trends concerning VCI neuroimaging. Objective: This study endeavors to explore the collaboration relationships of authors, countries, and institutions, as well as the research hotspots and frontiers of VCI neuroimaging by conducting a bibliometric analysis. Methods: We performed a comprehensive retrieval within the Core Collection of Web of Science, spanning from 2000 to 2023. After screening the included literature, CiteSpace and VOSviewer were utilized for a visualized analysis aimed at identifying the most prolific author, institution, and journal, as well as extracting valuable information from the analysis of references. Results: A total of 1,024 publications were included in this study, comprising 919 articles and 105 reviews. Through the analysis of keywords and references, the research hotspots involve the relationship between neuroimaging of cerebral small vessel disease (CSVD) and VCI, the diagnosis of VCI, and neuroimaging methods pertinent to VCI. Moreover, potential future research directions encompass CSVD, functional and structural connectivity, neuroimaging biomarkers, and lacunar stroke. Conclusion: The research in VCI neuroimaging is constantly developing, and we hope to provide insights and references for future studies by delving into the research hotspots and frontiers within this field. [ABSTRACT FROM AUTHOR]

The aging population in Canada has been increasing continuously throughout the past decades. Amongst this demographic, around 11% suffer from some form of cognitive decline. While diagnosis through traditional means (i.e., Magnetic Resonance Imagings (MRIs), positron emission tomography (PET) scans, cognitive assessments, etc.) has been successful at detecting this decline, there remains unexplored measures of cognitive health that could reduce stress and cost for the elderly population, including approaches for early detection and preventive methods. Such efforts could additionally contribute to reducing the pressure and stress on the Canadian healthcare system, as well as improve the quality of life of the elderly population. Previous evidence has demonstrated emotional facial expressions being altered in individuals with various cognitive conditions such as dementias, mild cognitive impairment, and geriatric depression. This review highlights the commonalities among these cognitive health conditions, and research behind the contactless assessment methods to monitor the health and cognitive well-being of the elderly population through emotion expression. The contactless detection approach covered by this review includes automated facial expression analysis (AFEA), electroencephalogram (EEG) technologies and heart rate variability (HRV). In conclusion, a discussion of the potentials of the existing technologies and future direction of a novel assessment design through fusion of AFEA, EEG and HRV measures to increase detection of cognitive decline in a contactless and remote manner will be presented. [ABSTRACT FROM AUTHOR]

Detecting cognitive impairments early after stroke is essential for appropriate referrals. Although recommended in stroke guidelines, early cognitive screening is not always implemented. We assessed whether the Montreal Cognitive Assessment (MoCA) adds diagnostic value compared to clinical observation alone. In addition, discharge destinations for stroke patients with and without cognitive deficits detected with the screening tool or the treatment team were explored. Forty-four stroke patients were screened with the MoCA during stroke unit admission. Their charts were studied for cognitive impairments reported by the stroke care team, who were blinded to screening scores. Proportions of detected cognitive deficits were compared between screening (score <26) and patient charts. Discharge destination distribution (home vs. rehabilitation) was explored. The proportion of cognitively impaired patients indicated by the MoCA (84%) and reported in patients' charts (25%) differed significantly (p < 0.001). The distribution of discharge destination did not suggest an association with the detection of cognitive deficits by the treatment team or the cognitive screening. The MoCA detects more cognitive deficits than clinical impression alone, emphasizing the importance of standard screening for cognitive impairments in acute stroke patients. Ultimately, systematic screening may enhance discharge planning and improve long-term outcomes. [ABSTRACT FROM AUTHOR]

Background: Mild cognitive deficits (MCD) emerge before the first episode of psychosis (FEP) and persist in the clinical high-risk (CHR) stage. This study aims to refine risk prediction by developing MCD models optimized for specific early psychosis stages and target populations. Methods: A comprehensive neuropsychological battery assessed 1059 individuals with FEP, 794 CHR, and 774 matched healthy controls (HCs). CHR subjects, followed up for 2 years, were categorized into converters (CHR-C) and non-converters (CHR-NC). The MATRICS Consensus Cognitive Battery standardized neurocognitive tests were employed. Results: Both the CHR and FEP groups exhibited significantly poorer performance compared to the HC group across all neurocognitive tests (all p < 0.001). The CHR-C group demonstrated poorer performance compared to the CHR-NC group on three sub-tests: visuospatial memory (p < 0.001), mazes (p = 0.005), and symbol coding (p = 0.023) tests. Upon adjusting for sex and age, the performance of the MCD model was excellent in differentiating FEP from HC, as evidenced by an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.895 (p < 0.001). However, when applied in the CHR group for predicting CHR-C (AUC = 0.581, p = 0.008), the performance was not satisfactory. To optimize the efficiency of psychotic risk assessment, three distinct MCD models were developed to distinguish FEP from HC, predict CHR-C from CHR-NC, and identify CHR from HC, achieving accuracies of 89.3%, 65.6%, and 80.2%, respectively. Conclusions: The MCD exhibits variations in domains, patterns, and weights across different stages of early psychosis and diverse target populations. Emphasizing precise risk assessment, our findings highlight the importance of tailored MCD models for different stages and risk levels. [ABSTRACT FROM AUTHOR]

Background: Understanding the development of cognitive impairment and its influencing factors in older adults is crucial for formulating early intervention strategies. Purpose: To identify the early dimensions of cognitive impairment and provide a comprehensive description of the trajectories of cognitive decline in older adults prior to death. Methods: Based on the data of 9883 older adults in the Chinese Longitudinal Healthy Longevity Survey from 2002 to 2018, a longitudinal item response theory (Longitudinal IRT) model including covariates was applied to estimate the following parameters. The items in which older adults encountered obstacles first had the least difficulty parameters (δ). The earlier the information curve of an item is lifted, the more information it provides in the early stages of cognitive impairment. Regression coefficient (β) represents the relative rate of cognitive decline. The cognitive impairment values estimated from the Longitudinal IRT were fitted to a mixed‐effects model to examine cognitive impairment trajectories. Results: 'Draw the figure on B Card' (δ = −0.816) was the most challenging item, followed by 'recalling 'clothes" (δ = 0.348) and 'recalling 'apples" (δ = 0.419), while 'name the 'pen" (δ = 4.402) was the simplest instruction for the old adults. The curves of the items in the recall dimension began to rise in the early stages of cognitive impairment. Cognitive impairment of older adults who were women (β = 0.061), elder (β = 0.111), smokers (β = 0.060), living in rural areas (β = 0.052), not participating in organised social activities (β = 0.092), suffering from hypertension (β = 0.022), hyperglycaemia (β = 0.035), dyslipidaemia (β = 0.314), low education levels (β = 0.128), manual labourers (β = 0.027), and eventual development of dementia (β = 0.212) exhibited a more accelerated progression. These individuals also had poorer cognitive trajectories. Conclusion: Recall is the earliest dimension of cognitive impairment. The subjects who were women, elder, smokers, living in rural areas, not participating in organised social activities, suffering from hypertension, hyperglycaemia, dyslipidaemia, low education, manual labourers, and eventually development of dementia, had a faster descending speed and poorer trajectories. [ABSTRACT FROM AUTHOR]

Introduction: The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention. Methods: This study included 2,288 participants with normal cognitive function from the Ma'anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level. Results: The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773–0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults. Conclusion: The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings. [ABSTRACT FROM AUTHOR]

Background 10-year atherosclerotic cardiovascular disease (ASCVD) risk scores were useful for predicting large vessel disease, but the relationships between them and cerebral small vessel disease (CSVD) were unclear. Our study aimed to evaluate associations of 10-year ASCVD risk scores with CSVD and its magnetic resonance imaging (MRI) markers. Methods Community-dwelling residents from the PolyvasculaR Evaluation for Cognitive Impairment and vaScular Events study were included in this cross-sectional study. At baseline, we collected data related to the Framingham Risk Score (FRS), pooled cohort equation (PCE), prediction for ASCVD risk in China (China-PAR) and Systematic COronary Risk Evaluation model 2 (SCORE2), and classified participants into low, moderate and high groups. Participants underwent brain MRI scans. We evaluated white matter hyperintensity (WMH), lacunes, cerebral microbleeds (CMBs) and enlarged perivascular spaces in basal ganglia (BG-EPVS) according to criteria of Wardlaw and Rothwell, and calculated total CSVD score and modified total CSVD score. Results A total of 3063 participants were included, and 53.5% of them were female. A higher FRS was associated with higher total CSVD score (moderate vs. low: cOR 1.89, 95% CI 1.53–2.34; high vs. low: cOR 3.23, 95%CI 2.62–3.97), and the PCE, China-PAR or SCORE2 score was positively related to total CSVD score (P  < 0.05). Moreover, higher 10-year ASCVD scores were associated with higher odds of WMH (P  < 0.05), lacunes (P  < 0.05), CMBs (P  < 0.05) and BG-EPVS (P  < 0.05). Conclusions The 10-year ASCVD scores were positively associated with CSVD and its MRI markers. These scores provided a method of risk stratification in the population with CSVD. [ABSTRACT FROM AUTHOR]

Background: Electrophysiological biomarkers are being examined as potential diagnostic measures of cognitive impairment and its manifestations for psychiatric nurses' use in the care of Alzheimer's disease (AD). However, there is no integrative review describing the themes from the current research about electrophysiological biomarkers and the developing relationship among the themes. Characterizing this developing relationship is imperative for any possible integration of biomarkers into the care of AD by psychiatric nurses. Objective: The purpose of this integrative review is to identify themes from the current research about electrophysiological biomarkers of AD and the developing relationship among the themes, the conceivable relational premise for psychiatric nurses to integrate electrophysiological biomarkers into the screening, assessment, diagnosis, and treatment of AD for the care of persons with AD. Methods: A literature search was executed with PUBMED (accessing Medline and Elsevier) and CINAHL databases that focused on studies about electrophysiological biomarkers of AD from 2015 to 2022. Twenty-seven peer-reviewed studies met this review's inclusion criteria. Results: Five themes emerged: (1) assessing/screening, (2) assessment differential, (3) diagnosing, (4) diagnostic accuracy, and (5) treating. These themes related sequentially and linearly, establishing a developing relationship about the risk, the onset, and the progression of AD. Discussion: Electrophysiological biomarkers associated to cognitive impairment in AD, supporting the accepted understanding of the symptoms of AD. Changes in behavior and functioning were not examined, limiting the possible integration of electrophysiological biomarkers. Further investigations are warranted with an expansion of the clinical symptoms and diverse study populations. [ABSTRACT FROM AUTHOR]

Background and Objectives The number of older adults diagnosed with dementia is expected to more than double by mid-century. Monitoring older adults' cognitive functioning is essential for the early detection of dementia, which can increase treatment efficacy and slow the disease progression. Current approaches to detect preclinical dementia are expensive, invasive, and are not accessible to everyone. This study aimed to determine whether technology use patterns could be used as an indicator of cognitive change. Research Design and Methods Using 6 waves (2015–2020) from the National Health and Aging Trends Study, we examined within-person and between-person effects of stopping 4 types of technology use on 4 cognitive outcomes. Results Our analyses showed that stopping the use of the Internet, computers, and tablets, and sending texts and e-mails were related to cognitive decline. Discussion and Implications These results suggest that monitoring technology use patterns may be an innovative approach that is economical and efficient to identify individuals at risk of cognitive decline. [ABSTRACT FROM AUTHOR]

Background Cortical thickness (CT) alterations, mismatch negativity (MMN) reductions, and cognitive deficits are robust findings in first-episode psychosis (FEP). However, most studies focused on medicated patients, leaving gaps in our understanding of the interrelationships between CT, MMN, neurocognition, and psychosocial functioning in unmedicated FEP. This study aimed to employ multiple mediation analysis to investigate potential pathways among these variables in unmedicated drug-naïve FEP. Methods We enrolled 28 drug-naïve FEP and 34 age and sex-matched healthy controls. Clinical symptoms, neurocognition, psychosocial functioning, auditory duration MMN, and T1 structural magnetic resonance imaging data were collected. We measured CT in the superior temporal gyrus (STG), a primary MMN-generating region. Results We found a significant negative correlation between MMN amplitude and bilateral CT of STG (CT_STG) in FEP (left: r  = −.709, P  < .001; right: r  = −.612, P  = .008). Multiple mediation models revealed that a thinner left STG cortex affected functioning through both direct (24.66%) and indirect effects (75.34%). In contrast, the effects of the right CT_STG on functioning were mainly mediated through MMN and neurocognitive pathways. Conclusions Bilateral CT_STG showed significant association with MMN, and MMN plays a mediating role between CT and cognition. Both MMN alone and its interaction with cognition mediated the effects of structural alterations on psychosocial function. The decline in overall function in FEP may stem from decreased CT_STG, leading to subsequent MMN deficits and neurocognitive dysfunction. These findings underline the crucial role of MMN in elucidating how subtle structural alterations can impact neurocognition and psychosocial function in FEP. [ABSTRACT FROM AUTHOR]

Background: Cognitive impairment associated with schizophrenia (CIAS) represents a distinct, persistent, and core group of schizophrenia symptoms. Cognitive symptoms have been shown to have an impact on quality of life. There are several published CIAS measures, but none based on direct patient self-report. It is important to capture the patient's perspective to supplement performancebased outcome measures of cognition to provide a complete picture of the patient's experience. This paper describes additional validation work on the Patient-Reported Experience of Cognitive Impairment in Schizophrenia (PRECIS) instrument. Methods: Data from two large, international, pharmaceutical clinical trials in medically and psychiatrically stable English-speaking patients with schizophrenia and 88 healthy controls were analyzed. An exploratory factor analysis (EFA) was conducted in one trial (n = 215), using the original 35-item PRECIS. The factor structure suggested by EFA was further evaluated using item response theory (IRT; Samejima's graded response model), and tested using confirmatory factor analysis (CFA). Both EFA and CFA results were tested in a second trial with similar inclusion/exclusion characteristics (n = 410). Additional statistical properties were evaluated in healthy controls. Results: EFA suggested that the best solution after item reduction suggested a factor structure of 6 factors based on 26 items (memory, communication, self-control, executive function, attention, sharpness of thought), supporting a total score, with an additional 2-item bother score (28 items in all). IRT analysis indicated the items were well-ordered within each domain. The CFA demonstrated excellent model fit, accounting for 69% of the variance. The statistical properties of the 28-item version of the PRECIS were confirmed in the second trial. Evidence for internal consistency and test-retest reliability was robust. Known-groups validity was supported by comparison of healthy controls with patients with schizophrenia. Correlations indicated moderate associations between PRECIS and functioning instruments like the Schizophrenia Cognition Rating Scale (SCoRS), but weak correlations with performance-based outcomes like MATRICS Consensus Cognitive Battery (MCCB). Discussion: Using two clinical trial samples, we identified a robust factor structure for the PRECIS and were able to replicate it in the second sample. Evaluation of the meaningful score difference (MSD) should be repeated in future studies, as these samples did not show enough change for it to be evaluated. Conclusions: This analysis provides strong evidence for the reliability and validity of the PRECIS, a 28-item, patient-reported instrument to assess cognitive impairment associated with schizophrenia. The correlation with functioning and the weak correlation with performance on cognitive tasks suggests that patient reports of cognitive impairment measure a unique aspect of patient experience. [ABSTRACT FROM AUTHOR]

Background and Objectives Proxy respondents are an important tool in survey research, especially among people with cognitive impairment. However, proxy respondents may be unable to accurately answer subjective survey instruments for cognitively impaired persons. This study investigates the mediating effect of proxy status on the relationship between cognitive impairment and subjectively rated health. Research Design and Methods Respondents from the 2018 wave of the Health and Retirement Study (N  = 17,146) were included, and the Baron and Kenny method assessed the potential mediating role of having a proxy on subjectively rated health. Subjectively rated health and proxy status were dichotomously coded, and relationships between cognitive impairment, proxy status, and subjectively rated health were assessed using logistic regression. Results Findings indicate that cognitive impairment is inversely associated with subjectively rated health, and it is directly associated with having a proxy. They also indicate that having a proxy is inversely related to subjectively rated health. When including proxy status and cognitive impairment in the same model, cognitive impairment no longer predicted subjectively rated health. This indicates that proxy status perfectly mediated the relationship between cognitive impairment and subjectively rated health. Discussion and Implications Measuring the experiences of people with cognitive impairment is challenging in survey research due to the limiting features of cognitive impairment. Although having a proxy respondent helps address attrition-related measurement challenges in survey research, findings indicate that proxies report worse subjectively rated health for people with cognitive impairment compared to individuals with comparable levels of cognitive impairment without a proxy. Future research may benefit from exploring optimal proxy-respondent characteristics. [ABSTRACT FROM AUTHOR]

Background: Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction. Objective: To identify plasma biomarkers for SCD. Methods: Fifty-two participants [38 with SCD, 14 healthy controls (HCs)] underwent baseline assessments, including measurements of plasma Aß42, Aß40, t-tau, p-tau, and a-synuclein using immunomagnetic reduction (IMR) assays, cognitive tests and the Mini-Mental State Examination (MMSE). Following initial cross-sectional analysis, 39 individuals (29 with SCD, 10 HCs) entered a longitudinal phase for reassessment of these biomarkers and the MMSE. Biomarker outcomes across different individual categories were primarily assessed using the area under the receiver operating characteristic (ROC) curve. The SCD subgroup with an MMSE decline over one point was compared to those without such a decline. Results: Higher baseline plasma Aß1-42 levels significantly discriminated participants with SCD from HCs, with an acceptable area under the ROC curve (AUC) of 67.5% [95% confidence interval (CI), 52.7-80.0%]. However, follow-up and changes in MMSE and IMR data did not significantly differ between the SCD and HC groups (p > 0.05). Furthermore, lower baseline plasma Aß1-42 levels were able to discriminate SCD subgroups with and without cognitive decline with a satisfied performance (AUC, 75.0%; 95% CI, 55.6-89.1%). At last, the changes in t-tau and Aß42 × t-tau could differentiate between the two SCD subgroups (p < 0.05). Conclusion: Baseline plasma Aß42 may help identify people with SCD and predict SCD progression. The role of plasma Aß42 levels as well as their upward trends from baseline in cases of SCD that progress to mild cognitive impairment and Alzheimer's disease require further investigation. [ABSTRACT FROM AUTHOR]

Neurodegenerative disorders such as Alzheimer's disease (AD) represent an increasingly significant public health concern. As clinical diagnosis faces challenges, biomarkers are becoming increasingly important in research, trials, and patient assessments. While biomarkers like amyloid‐β peptide, tau proteins, CSF levels (Aβ, tau, and p‐tau), and neuroimaging techniques are commonly used in AD diagnosis, they are often limited and invasive in monitoring and diagnosis. For this reason, blood‐based biomarkers are the optimal choice for detecting neurodegeneration in brain diseases due to their noninvasiveness, affordability, reliability, and consistency. This literature review focuses on plasma neurofilament light (NfL) and CSF NfL as blood‐based biomarkers used in recent AD diagnosis. The findings revealed that the core CSF biomarkers of neurodegeneration (T‐tau, P‐tau, and Aβ42), CSF NFL, and plasma T‐tau were strongly associated with Alzheimer's disease, and the core biomarkers were strongly associated with mild cognitive impairment due to Alzheimer's disease. Elevated levels of plasma and cerebrospinal fluid NfL were linked to decreased [18F]FDG uptake in corresponding brain areas. In participants with Aβ positivity (Aβ+), NfL correlated with reduced metabolism in regions susceptible to Alzheimer's disease. In addition, CSF NfL levels correlate with brain atrophy and predict cognitive changes, while plasma total tau does not. Plasma P‐tau, especially in combination with Aβ42/Aβ40, is promising for symptomatic AD stages. Though not AD‐exclusive, blood NfL holds promise for neurodegeneration detection and assessing treatment efficacy. Given the consistent levels of T‐tau, P‐tau, Aβ42, and NFL in CSF, their incorporation into both clinical practice and research is highly recommended. [ABSTRACT FROM AUTHOR]

Disabled older adults exhibited a higher risk for cognitive impairment. Early identification is crucial in alleviating the disease burden. This study aims to develop and validate a prediction model for identifying cognitive impairment among disabled older adults. A total of 2138, 501, and 746 participants were included in the development set and two external validation sets. Logistic regression, support vector machine, random forest, and XGBoost were introduced to develop the prediction model. A nomogram was further established to demonstrate the prediction model directly and vividly. Logistic regression exhibited better predictive performance on the test set with an area under the curve of 0.875. It maintained a high level of precision (0.808), specification (0.788), sensitivity (0.770), and F1-score (0.788) compared with the machine learning models. We further simplified and established a nomogram based on the logistic regression, comprising five variables: age, daily living activities, instrumental activity of daily living, hearing impairment, and visual impairment. The areas under the curve of the nomogram were 0.871, 0.825, and 0.863 in the internal and two external validation sets, respectively. This nomogram effectively identifies the risk of cognitive impairment in disabled older adults. [ABSTRACT FROM AUTHOR]

Background and objectives People with parkinsonism who are older, living in a care home, with frailty, multimorbidity or impaired capacity to consent are under-represented in research, limiting its generalisability. We aimed to evaluate more inclusive recruitment strategies. Methods From one UK centre, we invited people with parkinsonism to participate in a cross-sectional study. Postal invitations were followed by telephone reminders and additional support to facilitate participation. Personal consultees provided information on the views regarding research participation of adults with impaired capacity. These approaches were evaluated: (i) using external data from the Parkinson's Real World Impact assesSMent (PRISM) study and Clinical Practice Research Datalink (CPRD), a sample of all cases in UK primary care, and (ii) comparing those recruited with or without intensive engagement. Results We approached 1,032 eligible patients, of whom 542 (53%) consented and 477 (46%) returned questionnaires. The gender ratio in PRIME-UK (65% male) closely matched CPRD (61% male), unlike in the PRISM sample (46%). Mean age of PRIME participants was 75.9 (SD 8.5) years, compared to 75.3 (9.5) and 65.4 (8.9) years for CPRD and PRISM, respectively. More intensive engagement enhanced recruitment of women (13.3%; 95% CI 3.8, 22.9%; P  = 0.005), care home residents (6.2%; 1.1, 11.2%; P  = 0.004), patients diagnosed with atypical parkinsonism (13.7%; 5.4, 19.9%; P  < 0.001), and those with a higher frailty score (mean score 0.2, 0.1, 0.2; P  < 0.001). Conclusions These recruitment strategies resulted in a less biased and more representative sample, with greater inclusion of older people with more complex parkinsonism. [ABSTRACT FROM AUTHOR]

Background: Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking. Aims: This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall. Methods & Procedures: This is a cross‐sectional, online, self‐administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence‐Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi‐QA‐MPNet (Multi‐Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence‐Transformer. Outcomes & Results: The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task. Conclusions & Implications: This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images. WHAT THIS PAPER ADDS: What is already known on this subject: It is already known that speech‐ and language‐based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech‐ and language‐based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge: This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work?: The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life. [ABSTRACT FROM AUTHOR]

Introduction: Early diagnosis of dementia is partly based on the evaluation of the basic (b-), instrumental (i-) and advanced (a-) activities of daily living (ADL). Adapting the item content of assessment tools to maintain relevance, understanding, and equivalence of the items and scales across other groups is pertinent. However, there are limited assessment tools developed on or adapted for use on the growing older Sub-Saharan African (SSA) migrant population in Europe. Focusing on the item content, we evaluated the relevance of the advanced activities of daily living (a-ADL) tool to the older SSA migrant population in Belgium. Method: Two focus group discussions were organized with participants from the SSA community in Gent, Belgium. Relevant, irrelevant, unclear, and new items were identified. The data was analyzed through the inductive method while consensus agreements were used to arrive at refined themes. Results: Out of the 49 items on the tool, both groups unanimously agreed that 15 items were relevant and 23 irrelevant. Thirteen new items were identified, whereas 15 items were considered unclear. Conclusion: Participants agreed the tool was relevant but should be adapted. Verification and validation of these results in a larger SSA older migrant population in Belgium is strongly recommended. [ABSTRACT FROM AUTHOR]

The worldwide incidence of cognitive impairment is escalating, yet no effective solutions for these afflictions have been discovered. Consequently, the importance of early identification and immediate intervention is heightened. Advanced eye movements--a form of voluntary eye movements that includes anti-saccades, memory-guided saccades, predictive saccades, pro-saccades and gap/overlap saccades, mediated by the cerebral cortex and subcortical pathways reflect cognitive levels and functions across different domains. In view of their objectivity, reproducibility, and non-invasive characteristics, advanced eye movement examination possesses significant prospective utility across a wide range of cognitive impairment. This paper extensively reviews various models associated with advanced eye movement examinations and their current applications in cognitive impairment such as Alzheimer's disease, Lewy body dementia and frontotemporal dementia. Advanced eye movement examination can serve as a biomarker for early screening diagnosis and research on cognitive impairment. In the future, combining advanced eye movement examination with neuropsychological scale assessment and other diagnostic methods may contribute to further early identification of these types of diseases. [ABSTRACT FROM AUTHOR]

Background: Vascular cognitive impairment (VCI) is a major cause of cognitive impairment in the elderly and a co-factor in the development and progression of most neurodegenerative diseases. With the continuing development of neuroimaging, multiple markers can be combined to provide richer biological information, but little is known about their diagnostic value in VCI. Methods: A total of 83 subjects participated in our study, including 32 patients with vascular cognitive impairment with no dementia (VCIND), 21 patients with vascular dementia (VD), and 30 normal controls (NC). We utilized resting-state quantitative electroencephalography (qEEG) power spectra, structural magnetic resonance imaging (sMRI) for feature screening, and combined them with support vector machines to predict VCI patients at different disease stages. Results: The classification performance of sMRI outperformed qEEG when distinguishing VD from NC (AUC of 0.90 vs. 0,82), and sMRI also outperformed qEEG when distinguishing VD from VCIND (AUC of 0.8 vs. 0,0.64), but both underperformed when distinguishing VCIND from NC (AUC of 0.58 vs. 0.56). In contrast, the joint model based on qEEG and sMRI features showed relatively good classification accuracy (AUC of 0.72) to discriminate VCIND from NC, higher than that of either qEEG or sMRI alone. Conclusion: Patients at varying stages of VCI exhibit diverse levels of brain structure and neurophysiological abnormalities. EEG serves as an affordable and convenient diagnostic means to differentiate between different VCI stages. A machine learning model that utilizes EEG and sMRI as composite markers is highly valuable in distinguishing diverse VCI stages and in individually tailoring the diagnosis. [ABSTRACT FROM AUTHOR]

Introduction: Assessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring. Methods: A study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 - Meal, Task 2 - Beverage and Task 3 - Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated. Results: The composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature "Activity Duration" in Task 1 - Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups. Discussion: This ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers. [ABSTRACT FROM AUTHOR]

Aim: This study aimed to examine the relationships between levels of competence and impaired physical and cognitive functions in older adults. Methods: We used a data set of the Integrated Longitudinal Studies on Aging in Japan for 2017 including 5475 community-dwelling older adults. Levels of competence were assessed using the Japan Science and Technology Agency Index of Competence (JST-IC). Grip strength (low grip strength: <28 kg for men and <18 kg for women) and gait speed (slow gait speed: <1.0 m/s for both sexes) were evaluated as physical function measurements, and the Mini-Mental State Examination (cognitive decline: <24 on the Mini-Mental State Examination) was used to assess cognitive function. Results: The JST-IC had areas under the curve estimated from receiver operating characteristic analysis ranging from 0.65 to 0.73 for detecting low function as assessed by these tests. Restricted cubic spline curves showed that the shape of the association between the JST-IC and impaired function depended on sex and the test used. The comparison between perfect and imperfect JST-IC scores showed significant differences in the prevalence of low grip strength in both sexes, slow gait speed in women, and cognitive decline in men. Conclusions: It may be insufficient to identify those with impaired physical or cognitive function using the JST-IC. The shape of the association with the JST-IC varies across their measurements. Our findings can help interpret JST-IC scores in the context of low physical and cognitive functions. [ABSTRACT FROM AUTHOR]