Bin Huang

Introduction: Early diagnosis of cognitive impairment and dementia relies on comprehensive, evidence-based cognitive assessments, which currently requires a clinic visit and access to skilled healthcare providers. This poses a challenge for people who live in areas with inadequate primary care services and those who have economic, insurance, or other transient hardships (transportation, time, etc.) that limit their access to healthcare services. Digital cognitive assessments (DCAs) with remote testing capabilities have emerged as an efficient and cost-effective solution. The aim of this study was to validate the reliability of BrainCheck, a platform for DCAs, when self-administered remotely.Methods: A total of 46 participants (60.9% female; age range 52–76) remotely completed a battery of six BrainCheck cognitive assessments twice on the same device (iPad = 8, iPhone = 5, laptop = 33): the participants self-administered in one session and were administered by a research coordinator (RC) in the other session. Thirty participants completed the self-administered session first, while 16 completed it second. The inter-session interval (ISI) varied across participants, from within the same day to 21 days apart. Testing outcomes, including the duration of time needed to complete the battery, the raw score from each assessment, and the raw overall score, were compared between the two sessions.Results: We found moderate or good agreement between self- and RC-administered performance, with intraclass correlation ranging from 0.59 to 0.83. Results from mixed-effects modeling further confirm the non-significant difference between self- vs. RC-administered testing performance, which is independent of other factors including testing order, ISI, device, and participants’ demographic characteristics.Discussion: These results demonstrate the feasibility of remote self-administration using BrainCheck.

Background: Dementia is characterized by impairments in an individual’s cognitive and functional abilities. Digital cognitive assessments have been shown to be effective in detecting mild cognitive impairment and dementia, but whether they can stage the disease remains to be studied. Objective: In this study, we examined (1) the correlation between scores obtained from BrainCheck standard battery of cognitive assessments (BC-Assess), a digital cognitive assessment, and scores obtained from the Dementia Severity Rating Scale (DSRS), and (2) the accuracy of using the BC-Assess score to predict dementia stage delineated by the DSRS score. We also explored whether BC-Assess can be combined with information from the Katz Index of Independence in activities of daily living (ADL) to obtain enhanced accuracy. Methods: Retrospective analysis was performed on a BrainCheck dataset containing 1751 patients with dementia with different cognitive and functional assessments completed for cognitive care planning, including the DSRS, the ADL, and the BC-Assess. The patients were staged according to their DSRS total score (DSRS-TS): 982 mild (DSRS-TS 10‐18), 656 moderate (DSRS-TS 19-26), and 113 severe (DSRS-TS 37-54) patients. Pearson correlation was used to assess the associations between BC-Assess overall score (BC-OS), ADL total score (ADL-TS), and DSRS-TS. Logistic regression was used to evaluate the possibility of using patients’ BC-OS and ADL-TS to predict their stage. Results: We found moderate Pearson correlations between DSRS-TS and BC-OS (r=−0.53), between DSRS-TS and ADL-TS (r=−0.55), and a weak correlation between BC-OS and ADL-TS (r=0.37). Both BC-OS and ADL-TS significantly decreased with increasing severity. BC-OS demonstrated to be a good predictor of dementia stages, with an area under the receiver operating characteristic curve (ROC-AUC) of classification using logistic regression ranging from .733 to .917. When BC-Assess was combined with ADL, higher prediction accuracies were achieved, with an ROC-AUC ranging from 0.786 to 0.961. Conclusions: Our results suggest that BC-Assess could serve as an effective alternative tool to DSRS for grading dementia severity, particularly in cases where DSRS, or other global assessments, may be challenging to obtain due to logistical and time constraints.

Introduction: Previous validation studies demonstrated that BrainCheck Assess (BC-Assess), a computerized cognitive test battery, can reliably and sensitively distinguish individuals with different levels of cognitive impairment (i.e., normal cognition (NC), mild cognitive impairment (MCI), and dementia). Compared with other traditional paper-based cognitive screening instruments commonly used in clinical practice, the Montreal Cognitive Assessment (MoCA) is generally accepted to be among the most comprehensive and robust screening tools, with high sensitivity/specificity in distinguishing MCI from NC and dementia. In this study, we examined: (1) the linear relationship between BC-Assess and MoCA and their equivalent cut-off scores, and (2) the extent to which they agree on their impressions of an individual’s cognitive status.Methods: A subset of participants (N = 55; age range 54–94, mean/SD = 80/9.5) from two previous studies who took both the MoCA and BC-Assess were included in this analysis. Linear regression was used to calculate equivalent cut-off scores for BC-Assess based on those originally recommended for the MoCA to differentiate MCI from NC (cut-off = 26), and dementia from MCI (cut-off = 19). Impression agreement between the two instruments were measured through overall agreement (OA), positive percent agreement (PPA), and negative percent agreement (NPA).Results: A high Pearson correlation coefficient of 0.77 (CI = 0.63–0.86) was observed between the two scores. According to this relationship, MoCA cutoffs of 26 and 19 correspond to BC-Assess scores of 89.6 and 68.5, respectively. These scores are highly consistent with the currently recommended BC-Assess cutoffs (i.e., 85 and 70). The two instruments also show a high degree of agreement in their impressions based on their recommended cut-offs: (i) OA = 70.9%, PPA = 70.4%, NPA = 71.4% for differentiating dementia from MCI/NC; (ii) OA = 83.6%, PPA = 84.1%, NPA = 81.8% for differentiating dementia/MCI from NC.Discussion: This study provides further validation of BC-Assess in a sample of older adults by showing its high correlation and agreement in impression with the widely used MoCA.

Previous validation studies demonstrated that BrainCheck Assess (BC-Assess), a computerized cognitive test battery, can reliably and sensitively distinguish individuals with different levels of cognitive impairment (i.e., normal cognition (NC), mild cognitive impairment (MCI), and dementia). Compared with other traditional paper-based cognitive screening instruments commonly used in clinical practice, the MoCA is generally accepted to be among the most comprehensive and robust screening tools, with high sensitivity/specificity in distinguishing MCI from NC and dementia. In this study, we examined: (1) the linear relationship between BC-Assess and MoCA and their equivalent cut-off scores, and (2) the extent to which they agree on their impressions of an individual's cognitive status.A subset of participants (N=55; age range 54-94, mean/SD=80/9.5) from two previous studies who took both the MoCA and BC-Assess were included in this analysis. Linear regression was used to calculate equivalent cut-off scores for BC-Assess based on those originally recommended for the MoCA to differentiate MCI from NC (cut-off=26), and dementia from MCI (cut-off=19). Impression agreement between the two instruments were measured through overall agreement (OA), positive percent agreement (PPA), and negative percent agreement (NPA). A high Pearson correlation coefficient of 0.77 (CI = 0.63 - 0.86) was observed between the two scores. According to this relationship, MoCA cutoffs of 26 and 19 correspond to BC-Assess scores of 89.6 and 68.5, respectively. These scores are highly consistent with the currently recommended BC-Assess cutoffs (i.e. 85 and 70). The two instruments also show a high degree of agreement in their impressions based on their recommended cut-offs: (i) OA=70.9%, PPA=70.4%, NPA=71.4% for differentiating dementia from MCI/NC; (ii) OA=83.6%, PPA=84.1%, NPA=81.8% for differentiating dementia/MCI from NC.This study provides further validation of BC-Assess in a sample of older adults by showing its high correlation and agreement in impression with the widely used MoCA.

With a growing aging cognitive impairment and dementia population and with no present cure, there is an increased focus on improving quality of life and health while decreasing disease burden and hospitalization. An effective way to support this focus is through cognitive care planning: routinely and systematically assessing patient needs while documenting personalized recommendations to address neuropsychiatric and neurocognitive symptoms, functional limitations, and care resources. While beneficial and reimbursable, cognitive care planning remains underutilized.By adopting a descriptive qualitative approach, we conducted semi-structured telephone interviews with nine primary care providers (seven identified as male; two identified as Asian American; work experience ranged from 8 to 38 years) who were actively providing care to older adults, including those with cognitive impairment and dementia in the Pacific Northwest area. Data were analyzed through content analysis. Seven categories of barriers and facilitators informed by the Consolidated Framework for Implementation Research (CFIR) were constructed from the interviews: (a) caregiver characteristics, (b) organization characteristics, (c) patient characteristics, (d) provider characteristics, (e) condition characteristics, (f) workflow, and (g) outer setting. Primary care providers described the barriers and facilitators experienced in cognitive care planning with patients. Primary care demands, time limitations, patient willingness, caregiver and family presence, lack of care planning structure, and uncertainty influence the implementation of cognitive care planning. These findings can help providers and clinical care teams support older adults with cognitive impairment and dementia and their caregivers facing a progressive illness.

Background and Objectives: A cochlear implant (CI) is an effective prosthetic device used to treat severe-to-profound hearing loss. The present study examined cognitive function in CI users by employing a web-based cognitive testing platform, i.e., BrainCheck, and explored the correlation between cognitive function and subjective evaluation of hearing. Subjects and Methods: Forty-two CI users (mean age: 58.90 years) were surveyed in the subjective evaluation of hearing, and 20/42 participated in the BrainCheck cognitive tests (immediate recognition, Trail Making A, Trail Making B, Stroop, digit symbol substitution, and delayed recognition). As controls for cognitive function, young normal-hearing (YNH, mean age=23.83 years) and older normal-hearing (ONH, mean age=52.67 years) listener groups were subjected to Brain-Check testing. Results: CI users exhibited poorer cognitive function than the normal hearing groups in all tasks except for immediate and delayed recognition. The highest percentage of CI users who had “possible” and “likely” cognitive impairment, based on BrainCheck scores (ranging from 0-200), was observed in tests assessing executive function. The composite cognitive score across domains tended to be related to subjective hearing (p=0.07). Conclusions: The findings of the current study suggest that CI users had a higher likelihood of cognitive impairment in the executive function domain than in lower-level domains. BrianCheck online cognitive testing affords a convenient and effective tool to self-evaluate cognitive function in CI users.

BACKGROUND Previous validation studies demonstrated that BrainCheck Assess (BC-Assess), a computerized cognitive test battery, can reliably and sensitively distinguish individuals with different levels of cognitive impairment (i.e., normal cognition (NC), mild cognitive impairment (MCI), and dementia). Compared with other traditional paper-based cognitive screening instruments commonly used in clinical practice, the MoCA is generally accepted to be among the most comprehensive and robust screening tools, with high sensitivity/specificity in distinguishing MCI from NC and dementia. OBJECTIVE In this study, we examined: (1) the linear relationship between BC-Assess and MoCA and their equivalent cut-off scores, and (2) the extent to which they agree on their impressions of an individual's cognitive status. METHODS A subset of participants (N=55; age range 54-94, mean/SD=80/9.5) from two previous studies who took both the MoCA and BC-Assess were included in this analysis. Linear regression was used to calculate equivalent cut-off scores for BC-Assess based on those originally recommended for the MoCA to differentiate MCI from NC (cut-off=26), and dementia from MCI (cut-off=19). Impression agreement between the two instruments were measured through overall agreement (OA), positive percent agreement (PPA), and negative percent agreement (NPA). RESULTS A high Pearson correlation coefficient of 0.77 (CI = 0.63 - 0.86) was observed between the two scores. According to this relationship, MoCA cutoffs of 26 and 19 correspond to BC-Assess scores of 89.6 and 68.5, respectively. These scores are highly consistent with the currently recommended BC-Assess cutoffs (i.e. 85 and 70). The two instruments also show a high degree of agreement in their impressions based on their recommended cut-offs: (i) OA=70.9%, PPA=70.4%, NPA=71.4% for differentiating dementia from MCI/NC; (ii) OA=83.6%, PPA=84.1%, NPA=81.8% for differentiating dementia/MCI from NC. CONCLUSIONS This study provides further validation of BC-Assess in a sample of older adults by showing its high correlation and agreement in impression with the widely used MoCA.

Background: Early detection of dementia is critical for intervention and care planning but remains difficult. Computerized cognitive testing provides an accessible and promising solution to address these current challenges. Objective: The aim of this study was to evaluate a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. Methods: A total of 99 participants diagnosed with dementia, mild cognitive impairment (MCI), or normal cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. Results: BrainCheck battery performance showed significant differences between the NC, MCI, and dementia groups, achieving 88% or higher sensitivity and specificity (ie, true positive and true negative rates) for separating dementia from NC, and 77% or higher sensitivity and specificity in separating the MCI group from the NC and dementia groups. Three-group classification found true positive rates of 80% or higher for the NC and dementia groups and true positive rates of 64% or higher for the MCI group. Conclusions: BrainCheck was able to distinguish between diagnoses of dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.

BACKGROUND Early detection of dementia is critical for intervention and care planning but remains difficult. Computerized cognitive testing provides an accessible and promising solution to address these current challenges. OBJECTIVE The aim of this study was to evaluate a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. METHODS A total of 99 participants diagnosed with dementia, mild cognitive impairment (MCI), or normal cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant performances on BrainCheck based on their diagnostic group. RESULTS BrainCheck battery performance showed significant differences between the NC, MCI, and dementia groups, achieving 88% or higher sensitivity and specificity (ie, true positive and true negative rates) for separating dementia from NC, and 77% or higher sensitivity and specificity in separating the MCI group from the NC and dementia groups. Three-group classification found true positive rates of 80% or higher for the NC and dementia groups and true positive rates of 64% or higher for the MCI group. CONCLUSIONS BrainCheck was able to distinguish between diagnoses of dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.