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Background As the population of aging people living with HIV (PWH) increases, many have faced neurocognitive problems. Cognitive assessment plays a crucial role as the initial step in cognitive care of this specific population. We aimed to determine the validity between a traditional paper-based and tablet-based cognitive assessment tool among aging Thai PWH. Methods PWH aged ≥ 50 years underwent cognitive assessment using the Thai-validated Montreal Cognitive Assessment (MoCA). Participants were randomly assigned to receive either the paper-based MoCA or the tablet-based MoCA (eMoCA) first. Two weeks later, participants returned to complete the alternate version of the MoCA. Pearson correlation was used to determine the strength of the relationship between the paper-based MoCA and the eMoCA scores. Concordance correlation coefficients (CCC) were calculated, and a Bland-Altman plot was employed to determine the level of agreement between the two testing methods. Additionally, MoCA scores were compared between individuals with and without prior touchscreen tablet experience. Results Among 46 participants included in the analysis, 12 (26.1%) had experience using a touchscreen tablet. The score discrepancy between the two MoCA versions ranged from − 8 to 6, with a mean (SD) difference of -1.33 (3.22). The Pearson correlation coefficient between the paper-based MoCA and the eMoCA was r  = 0.54 ( p  = 0.001), with a concordance correlation coefficient of 0.47. The Bland-Altman plot showed 95% limits of agreement between − 7.63 and 4.98. Among participants with prior touchscreen tablet experience, scores between the paper-based MoCA and the eMoCA were comparable. However, those without prior touchscreen experience had significantly lower scores on the eMoCA compared to the paper-based MoCA (mean difference − 1.56, 95% CI -2.72 to -0.40). Conclusions The eMoCA demonstrated moderate correlation with the paper-based MoCA, with prior touchscreen tablet experience significantly affecting the validity of the MoCA scores between the two versions. Clinicians should consider individuals’ level of touchscreen experience before selecting the administration modality.

Objective The aim of this study was to compare the Montreal Cognitive Assessment (MoCA) performances of people who report no, subclinical, and clinical symptoms of depression. Methods Data was collected for the randomized controlled trial BrainFit-Nutrition. A secondary data analysis of 1,111 participants (age ≥ 60 years; M  = 68.4 years; 55.1% female) was performed. Depressive symptoms were assessed with the Patient Health Questionnaire-9 (PHQ-9), cognitive performance was assessed via the MoCA. Performance differences were tested with Kruskal-Wallis tests. Two sensitivity analyses were conducted, one with data from people with MCI and one with the original item structure of the MoCA. Results No differences were found in the MoCA total score or in visuospatial, executive functioning, attention, memory, or orientation subscores between individuals with no, subclinical, or clinical symptoms of depression. A sensitivity analysis also showed no differences. Conclusion Cognitive screening with the MoCA seems to be robust against depression and could therefore be used to screen for MCI regardless of depression level. Trial registration The study was prospectively registered at the International Standard Randomized Controlled Trial Number Registry on 23/11/2021 (ISRCTN 10560738).

ABSTRACTObjectiveTo compare the accuracy of Mini-Mental State Examination (MMSE) and of the Montreal Cognitive Assessment (MoCA) in tracking mild cognitive impairment (MCI) and Alzheimer’s Disease (AD). MethodA Systematic review of the PubMed, Bireme, Science Direct, Cochrane Library, and PsycInfo databases was conducted. Using inclusion and exclusion criteria and staring with 1,629 articles, 34 articles were selected. The quality of the selected research was evaluated through the Quality Assessment of Diagnostic Accuracy Studies 2 tool (QUADAS-2). ResultMore than 80% of the articles showed MoCA to be superior to MMSE in discriminating between individuals with mild cognitive impairment and no cognitive impairment. The area under the curve varied from 0.71 to 0.99 for MoCA, and 0.43 to 0.94 for MMSE, when evaluating the ability to discriminate MCI in the cognitively healthy elderly individuals, and 0.87 to 0.99 and 0.67 to 0.99, respectively, when evaluating the detection of AD. The AUC mean value for MoCA was significantly larger compared to the MMSE in discriminating MCI from control [0.883 (CI 95% 0.855-0.912) vs MMSE 0.780 (CI 95% 0.740-0.820) p < 0.001]. ConclusionThe screening tool MoCA is superior to MMSE in the identification of MCI, and both tests were found to be accurate in the detection of AD.

The Mini-Mental State Examination (MMSE) is a widely used cognitive instrument that is validated and translated into many languages. In clinical trials, it is frequently utilized as an eligibility screening tool, though less commonly as an endpoint. Despite its broad use, administration and scoring errors are prevalent, stemming from factors such as differing scale and scoring guidelines, varying levels of rater experience, and inconsistent application of scoring criteria. This study explores common administration and scoring errors identified among site raters in multinational Alzheimer's disease clinical trials. Central reviews of MMSE assessments were conducted in two multinational Phase 3 Alzheimer's disease trials, where the MMSE was administered during the screening visit. Trained, qualified, and calibrated central reviewers performed audio and data reviews of the MMSE assessments. Errors in administration and scoring were identified using \"flags,\" with detailed descriptions provided for each flagged error. Frequencies of flagged errors were calculated. Both studies utilized the same set of flags and the same training materials for the scale to ensure consistency. A total of 10,203 MMSE assessments were reviewed, with 26.8% flagged for administration errors and 27.0% for scoring errors. The frequency of flagged errors is summarized in Table 1. The five most common errors, in descending order, were: administration errors in Orientation to Place (11.8%), administration errors in Attention and Calculation (9.7%), scoring errors in Orientation to Place (9.5%), administration errors in Orientation to Time (7.0%), and administration errors in Writing (5.4%). Although the MMSE is widely recognized and utilized by clinicians, our study revealed that errors in its administration and scoring are relatively common. Non-standardized practices can introduce variability and noise into clinical trial data, potentially compromising study outcomes. Addressing these issues requires the implementation of robust data quality monitoring strategies, such as central reviews and blinded data analysis, to promptly identify and correct errors, prevent recurrence, and ensure consistent rater calibration. Additionally, incorporating these findings into rater training programs can effectively reduce common errors, ultimately improving the accuracy and overall quality of data in clinical trials.

ObjectivesDeveloping disease modifying therapies for Parkinson’s disease (PD) calls for outcome measurement strategies focused on characterizing early stage disease progression. We explored the psychometric evidence for using the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) part II (patient motor experience of daily living) and part III (clinician motor examination) in this context.MethodsMDS-UPDRS-II and -III data were collected at screening, month 12, and month 24 from 384 early stage PD patients (diagnosis ≤ 2 years; Hoehn and Yahr stage 1/2) in the Parkinson’s Progression Markers Initiative (PPMI) study. Psychometric analysis, based on Rasch measurement theory (RMT), was performed on both the original MDS UPDRS-II and -III scales and exploratory content-driven scale structures.ResultsRMT analyses showed neither scale was well targeted to early PD. A marked floor effect appeared for most items and a clear item gap was consistently observed in very mild severity of motor signs and levels of motor impact. The original MDS-UPDRS-II and -III scales also displayed disordered thresholds (9/13 and 20/33 items, respectively), indicating response scales not functioning as expected, and misfit (5/13 and 12/33 items, respectively), flagging areas for potential improvement.ConclusionsThe MDS-UPDRS-II and -III have psychometric limitations which limits the precision of measurement of motor symptoms and impact in early PD. This can lead to insensitivity in detecting differences and clinical change. Importantly, the diagnostic psychometric evidence provided by the RMT analysis provides a clear starting point for how to improve the quantification of clinically relevant concepts to characterize the course of early PD.

Background and purpose The aim of this study was to examine the discriminative validity of the Brief Assessment of Impaired Cognition (BASIC) case‐finding instrument in a general practice (GP) setting and compare it with other widely used brief cognitive instruments. Methods Patients aged ≥70 years were prospectively recruited from 14 Danish GP clinics. Participants were classified as having either normal cognition (n = 154) or cognitive impairment (n = 101) based on neuropsychological test performance, reported instrumental activities of daily living, and concern regarding memory decline. Comparisons involved the Mini‐Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), the Rowland Universal Dementia Assessment Scale (RUDAS), the Mini‐Cog, the 6‐item Clock Drawing Test (CDT‐6) and the BASIC Questionnaire (BASIC‐Q). Results BASIC demonstrated good overall classification accuracy with an area under the receiver operating characteristic curve (AUC) of 0.88 (95% confidence interval [CI] 0.84–0.92), a sensitivity of 0.72 (95% CI 0.62–0.80) and a specificity of 0.86 (95% CI 0.79–0.91). Pairwise comparisons of the AUCs of BASIC, MMSE, MoCA and RUDAS produced non‐significant results, but BASIC had significantly higher classification accuracy than Mini‐Cog, BASIC‐Q and CDT‐6. Depending on the pretest probability of cognitive impairment, the positive predictive validity of BASIC varied from 0.83 to 0.36, and the negative predictive validity from 0.97 to 0.76. Conclusions BASIC demonstrated good discriminative validity in a GP setting. The classification accuracy of BASIC is equivalent to more complex, time‐consuming instruments, such as the MMSE, MoCA and RUDAS, and higher than very brief instruments, such as the CDT‐6, Mini‐Cog and BASIC‐Q.

Clinical trials in Alzheimer's Disease (AD) suffer from high failure rates, in part due to imprecision in endpoint measurements that introduces noise . SIA is a quantitative approach that utilizes algorithms to identify inconsistencies in measurements that may be indicative of problematic scale administration and/or scoring errors. The CDR, a sole primary and key secondary endpoint in many AD trials, can be challenging to score, particularly in early symptomatic and mild diseases . The goal of this study was to develop and validate SIA algorithms for CDR, and to evaluate their incidence and association with scoring errors. Aggregated data from 40,148 CDR reviewed assessments across 34 multinational trials of pre-clinical, early symptomatic, and mild to moderate dementia due to AD were analyzed. Algorithms indicative of scoring errors on the basis of clinical judgement were developed. These were then subjected to a validation procedure to determine both the rates flag trigger, and the degree to which each flag was associated with increased scoring errors. The development and validation of one of these flags (the cognitive-functional difference score) is described in detail as an example. Algorithms that fire relatively infrequently (to minimize false positives), independently (indicating increased probability of scoring errors), and associated with increased error rates were identified. Five flags emerged from the validation process to meet criteria of relatively low firing rate (<10%), association with increased rates of scoring errors, and having relatively orthogonal relationships with each other. The latter goal was established by a strong linear relationship between number of flags triggered and scoring error rate, such that as the number of flags triggered increased, the mean error rate was significantly elevated (estimate = 0.20, SE = 0.008; p < 0.0001). The present study, using a large, aggregated dataset across multiple AD trials, demonstrated the utility of SIA algorithms in detecting data inconsistencies that help identify problematic assessments in CDR. The very strong association between flags triggered and error rates suggests that the algorithms can serve as a proxy for identifying assessments that are predictive of scoring errors and to surface these assessments for review/remediation.

Detecting cognitive decline presents a significant challenge for researchers and clinicians, particularly in older adults with low educational levels or different sociocultural characteristics. Additionally, traditional normative data may under- or overestimate preliminary diagnoses in these populations, due its non-use of measures which impact cognitive performance as years of schooling or age. This study aimed to validate and standardize the Brain Health Assessment Tablet-Based Cognitive Assessment Tool (BHA-TabCAT) among Andean Peruvian older adults. A total of 258 participants between the ages of 54 and 91 were assessed with BHA-TabCAT, the Mini Mental State Examination (MMSE) and the Rowland Universal (RUDAS). Participants were evaluated in the rural district of Pampacolca, and in the urban district of Arequipa, both located in the Andean city of Arequipa - Peru. All participants were assigned a clinical diagnosis group (cognitively health or mild cognitive impairment). We used regression-based norming procedures, including sex, place of residence, age and years of schooling as covariates. We followed the recommendations of Tsoy et al. (2021). Initially, the BHA-CS's analysis was performed considering weighted scores of significant and non-significant sociodemographic predictors. AUC captures the 75.3% of positive cases, against the MMSE (Specificity = 81.7%, Sensitivity = 48.6%, Accuracy = 72.5%, AUC = 66.4%) and RUDAS (Specificity = 76.3%, Sensitivity = 62.5%, Accuracy = 72.5% AUC = 71.4%). A second analysis was conducted using only the weighted scores of significant sociodemographic predictors. AUC of the BHA-CS captures 77.4% of the positive cases against the MMSE (Specificity = 77.4%, Sensitivity = 52.8%, Accuracy = 70.5%, AUC = 66.6%) and RUDAS (Specificity = 75.3%, Sensitivity = 66.7% Accuracy = 72.9%, AUC = 71.9%). The BHA-TabCAT shows better performance than the MMSE and RUDAS in detecting cognitive decline, especially when its scoring and interpretation consider relevant covariates such as years of education, age, place of residence, and gender. This provides improved opportunities to accurately identify the preclinical and prodromal stages of cognitive decline in both urban and rural contexts in Peru.

Background and purpose Post-stroke cognitive impairment (PSCI) is one of the serious complications of stroke, which profoundly influences the quality of life of stroke survivors. The Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) are the two cognitive screening tools most widely used in stroke settings. Previous studies investigated the diagnostic accuracy of MoCA and MMSE but yielded controversial results. We conducted this study to compare the diagnostic accuracy of MoCA with MMSE for PSCI. Methods Embase, PubMed, CINAHL, Web of Science, and The Cochrane Library were searched until August 17, 2024 for diagnostic accuracy studies comparing MoCA and MMSE. Data extraction was performed by two independent researchers. Risk of bias and applicability assessment was evaluated by the Quality Assessment of Diagnostic Accuracy Studies-2 tool. Coupled forest plots and hierarchical summary receiver operating characteristic (hsROC) curves were created in R statistical software. Results 9 studies with 1,135 patients were included in this review. Most studies were at high risk of bias. MoCA displayed a pooled sensitivity of 0.80 (95% CI 0.72 to 0.86) and specificity of 0.79 (95% CI 0.71 to 0.85). MMSE displayed a sensitivity and specificity of 0.76 (95% CI 0.71 to 0.81) and 0.78 (95% CI 0.73 to 0.83), respectively. No difference was shown between these modalities (SEN p  = 0.36, SPE p  = 0.80). Conclusion No difference was observed between MoCA and MMSE in the detection of PSCI. We recommend both screeners be considered for the detection of PSCI based on the purpose of the test and by other metrics, such as acceptability and feasibility. Although it should be noted MoCA and MMSE were cognitive screening tools in stroke settings and not a substitute for detailed clinical assessment.

Test-retest reliability for existing cognitive screening tests is typically poor and most have ceiling effects and restricted score ranges that mask the presence of subtle decline. The Brief Assessment of Cognitive Health (BACH) is a computerized cognitive screening tool that patients complete independently. It includes a complex memory test without ceiling effects and brief mood and history questions. The BACH generates a probability score for cognitive impairment that is highly accurate at predicting impairment on neuropsychological testing. The goal of this study was to determine if the psychometric characteristics of BACH (i.e., test-retest reliability and sensitivity to cognitive change) are superior compared to a commonly used screening test, the Montreal Cognitive Assessment (MoCA). Ninety-seven participants completed the BACH and MoCA at two timepoints. A mixed effects model was fit to derive between- and within-subjects variability to calculate the intraclass correlation coefficient (ICC) to assess test-retest reliability of the screening tools. A subset of 52 participants completed the same neuropsychological battery at both timepoints and individual composite cognitive change scores were calculated. Pearson correlations were used to determine the strength of relationships between the composite cognitive change score and change scores on the MoCA and BACH. On average, the ICC sample was 68 years-old with 15 years education, and 56% were female. The median time between test sessions was 384 days (range 246-1211). ICC for the BACH probability of impairment score was 0.59 (moderate reliability) whereas the ICC for the MoCA was 0.48 (poor reliability). For the cognitive testing sample (average age = 72 years, 16 years ed, 54% female), median time between test sessions was 336 days (range 263-426). Composite cognitive change score was moderately related to BACH probability change (r = -0.49; CI = [-0.68, -0.26]) and strongly related to BACH memory score change (r = 0.55; CI = [0.32, 0.71]). Composite cognitive change score was weakly associated with MoCA change score (r = 0.12; CI = [-0.17, 0.38]). The BACH demonstrated moderate to strong test-retest reliability and sensitivity to cognitive change, while observed MoCA psychometrics were below the cutoffs recommended for clinical practice. The BACH is a more accurate tool for cognitive surveillance in older adults.