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Objective:The purpose of this exploratory study was to describe associations between NIH Toolbox-Cognition Battery subtests and legacy measures of neurocognitive function in two samples with neurological conditions (stroke and sickle cell disease (SCD)).Method:This exploratory secondary analysis uses data from two studies that assessed cognition at one time point using the NIH Toolbox-Cognition Battery, the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS), and subtests from the Delis-Kaplan Executive Functions System (DKEFS). People with stroke (n = 26) and SCD (n = 64) were included. Associations between the NIH Toolbox-Cognition Battery subtests and corresponding legacy measures were examined using linear correlations, Bland–Altman analysis, and Lin’s Concordance Correlation Coefficient.Results:Linear correlations and Lin’s Concordance Correlation Coefficient were poor to strong in both samples on NIH Toolbox-CB subtests: Flanker Inhibitory Control and Attention (r = .35 to .48, Lin CCC = .27 to .37), Pattern Comparison Processing Speed (r = .40 to .65, Lin CCC = .37 to .62), Picture Sequence Memory (r = .19 to .55, Lin CCC = .18 to .48), Dimensional Change Card Sort (r = .39 to .77, Lin CCC = .38 to .63), Fluid Cognition Composite (r = .88 to .90, Lin CCC = .60 to .79), and Total Cognition Composite (r = .64 to .83, Lin CCC = .60 to .78). Bland–Altman analyses demonstrated wide limits of agreement across all subtests (–3.17 to 3.78).Conclusions:The NIH Toolbox-Cognition Battery subtests may behave similarly to legacy measures as an overall assessment of cognition across samples at risk for neurological impairment. Findings should be replicated across additional clinical samples.

Objectives: The purpose of this exploratory study was to identify clinical predictors that could distinguish clients’ level of engagement in inpatient rehabilitation following stroke. Methods: This is a secondary analysis of pooled data from three randomized controlled trials that examined the effects of a behavioral intervention. The sample (n=208) consisted of clients with stroke who had cognitive deficits (Quick-EXIT≥3) and were admitted to inpatient rehabilitation facilities associated with a university medical center. Individuals with pre-morbid dementia, aphasia and mood disorders were excluded. The Pittsburgh Rehabilitation Participation Scale was used to measure engagement. Clinical predictors were measured using the Functional Independence Measure, National Institutes of Health Stroke Scale, Repeatable Battery for the Assessment of Neuropsychological Status, selected subtests of the Delis-Kaplan Executive Function System, Patient Health Questionnaire-9, and Chedoke McMaster Stroke Assessment. Simple logistic regression identified individual clinical predictors associated with engagement. Hierarchical logistic regression identified the strongest predictors of engagement. Results: Impairments in executive functions [mean D-KEFS, odds ratio (OR)=4.062; 95% confidence interval (CI)=.866, 19.051], impairments in visuospatial skills (RBANS Visuospatial Index Score, OR=3.940; 95% CI=1.317, 11.785), impairments in mood (Patient Health Questionnaire-9, OR=2.059, 95% CI=.953, 4.449), and male gender (OR=2.474; 95% CI=1.145, 5.374) predicted levels of engagement in inpatient rehabilitation after controlling for study intervention group, baseline stroke severity, and baseline disability. Conclusions: Executive functions, visuospatial skills, mood, and gender distinguished individuals with high or low engagement in inpatient rehabilitation following stroke. Further studies should examine additional factors that may influence engagement (therapist-client relationship, treatment expectancy). (JINS, 2018, 24, 572–583)