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.

Early detection of dementia is critical for intervention and care planning, but remains difficult. This study evaluated a computerized cognitive testing battery (BrainCheck) for its diagnostic accuracy and ability to distinguish the severity of cognitive impairment. 99 participants diagnosed with Dementia, Mild Cognitive Impairment (MCI), or Normal Cognition (NC) completed the BrainCheck battery. Statistical analyses compared participant’s performance on BrainCheck based on their diagnosis group BrainCheck battery performance showed significant differences between the NC, MCI, and Dementia groups, achieving ≥88% sensitivity/specificity for separating NC from Dementia, and ≥77% sensitivity/specificity in separating the MCI group from NC/Dementia groups. Three-group-classification found true positive rates ≥80% for the NC and Dementia groups and ≥60% for the MCI group. BrainCheck was able to distinguish between diagnoses of Dementia, MCI, and NC, providing a potentially reliable tool for early detection of cognitive impairment.

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Objective To investigate the relationship between sleep duration and activities of daily living (ADL) disability, and to explore the optimal sleep duration among oldest-old Chinese individuals. Methods In this cross-sectional study, 1,798 participants (73.2% female) were recruited from Dongxing and Shanglin in Guangxi Zhuang Autonomous Region, China in 2019. The restricted cubic spline function was used to assess the dose-response relationship between sleep duration and ADL disability, and the odds ratios (ORs) of the associations were estimated by logistic regression models. Results The overall prevalence of ADL disability was 63% (64% in females and 58% in males). The prevalence was 71% in the Han population (72% in females and 68% in males), 60% in the Zhuang population (62% in females and 54% in males) and 53% in other ethnic population (53% in females and 53% in males). A nonlinear relationship between sleep duration and ADL disability was observed. Sleep duration of 8-10 hours was associated with the lowest risk of ADL disability. Sleep duration (≥12 hours) was associated with the risk of ADL disability among the oldest-old individuals after adjusting for confounding factors (OR = 1.47, 95% CI [1.02, 2.10], p < 0.05). Conclusion Sleep duration more than 12 hours may be associated with an increased risk of ADL disability in the oldest-old individuals, and the optimal sleep duration among this population could be 8–10 h.

Despite its high frequency of occurrence, mild traumatic brain injury (mTBI), or concussion, is difficult to recognize and diagnose, particularly in pediatric populations. Conventional methods to diagnose mTBI primarily rely on clinical questionnaires and sometimes include imaging such as computed tomography (CT) or pencil and paper neuropsychological testing. However, these methods are time consuming, require administration/interpretation from health professionals, and lack adequate test sensitivity and specificity. We explore the use of BrainCheck, a computerized neurocognitive test that is available on iPad, iPhone or computer desktop, for mTBI assessment. The BrainCheck battery consists of 6 gamified traditional neurocognitive tests that assess areas of cognition vulnerable to mTBI such as attention, processing speed, executing functioning, and coordination. We administered BrainCheck to 25 participants diagnosed with mTBI at the emergency department (ED) of Children's hospital within 96 hours of admittance to the ED, and 153 normal controls at a local high school. Statistical analysis included Chi-Square tests, Analysis of Variance (ANOVA), independent sample t-tests, and Hochberg tests to examine differences between mTBI, diagnoses by current gold standard clinical exam, and control groups on each assessment in the battery. Significant metrics from these assessments were used to build a logistic regression model that distinguishes mTBI from non-mTBI participants. Receiver operator score (ROC) analysis of our logistic regression model found a sensitivity of 84% and specificity of 80%. BrainCheck has potential in distinguishing mTBI from non-mTBI participants, by providing a shorter, gamified test battery to assess cognitive function after brain injury, while also providing a method for tracking recovery with the opportunity to do so remotely from a patient's home.