With the increased use of computer-based tests in clinical and research settings, assessing retest reliability and reliable change of NIH Toolbox-Cognition Battery (NIHTB-CB) and Cogstate Brief Battery (Cogstate) is essential. Previous studies used mostly White samples, but Black/African Americans (B/AAs) must be included in this research to ensure reliability.

Participants were B/AA consensus-confirmed healthy controls (HCs) (n = 49) or mild cognitive impairment (MCI) (n = 34) adults 60–85 years that completed NIHTB-CB and Cogstate for laptop at two timepoints within 4 months. Intraclass correlations, the Bland-Altman method, t-tests, and the Pearson correlation coefficient were used. Cut scores indicating reliable change provided.

NIHTB-CB composite reliability ranged from .81 to .93 (95% CIs [.37–.96]). The Fluid Composite demonstrated a significant difference between timepoints and was less consistent than the Crystallized Composite. Subtests were less consistent for MCIs (ICCs = .01–.89, CIs [−1.00–.95]) than for HCs (ICCs = .69–.93, CIs [.46–.92]). A moderate correlation was found for MCIs between timepoints and performance on the Total Composite (r = -.40, p = .03), Fluid Composite (r = -.38, p = .03), and Pattern Comparison Processing Speed (r = -.47, p = .006).

On Cogstate, HCs had lower reliability (ICCs = .47–.76, CIs [.05–.86]) than MCIs (ICCs = .65–.89, CIs [.29–.95]). Identification reaction time significantly improved between testing timepoints across samples.

The NIHTB-CB and Cogstate for laptop show promise for use in research with B/AAs and were reasonably stable up to 4 months. Still, differences were found between those with MCI and HCs. It is recommended that race and cognitive status be considered when using these measures.

Identify which NIH Toolbox Cognition Battery (NIHTB-CB) subtest(s) best differentiate healthy controls (HC) from those with amnestic mild cognitive impairment (aMCI) and compare the discriminant accuracy between a model using a priori “Norm Adjusted” scores versus “Unadjusted” standard scores with age, sex, race/ethnicity, and education controlled for within the model. Racial differences were also examined.

Participants were Black/African American (B/AA) and White consensus-confirmed (HC = 96; aMCI = 62) adults 60–85 years old that completed the NIHTB-CB for tablet. Discriminant function analysis (DFA) was used in the Total Sample and separately for B/AA (n = 80) and White participants (n = 78).

Picture Sequence Memory (an episodic memory task) was the highest loading coefficient across all DFA models. When stratified by race, differences were noted in the pattern of the highest loading coefficients within the DFAs. However, the overall discriminant accuracy of the DFA models in identifying HCs and those with aMCI did not differ significantly by race (B/AA, White) or model/score type (Norm Adjusted versus Unadjusted).

Racial differences were noted despite the use of normalized scores or demographic covariates—highlighting the importance of including underrepresented groups in research. While the models were fairly accurate at identifying consensus-confirmed HCs, the models proved less accurate at identifying White participants with an aMCI diagnosis. In clinical settings, further work is needed to optimize computerized batteries and the use of NIHTB-CB norm adjusted scores is recommended. In research settings, demographically corrected scores or within model correction is suggested.