Introduction
As the population with dementia has grown, disparities have emerged in the prevalence of all cause dementia among different races. Older Black/African Americans (B/AAs) are disproportionately more likely than older Whites to have Alzheimer’s disease (AD) and other dementias (Dilworth-Anderson et al., Reference Dilworth-Anderson, Hendrie, Manly, Khachaturian and Fazio2008; Power et al., Reference Power, Bennett, Turner, Dowling, Ciarleglio, Glymour and Gianattasio2021; Steenland et al., Reference Steenland, Goldstein, Levey and Wharton2016; Yaffe et al., Reference Yaffe, Falvey, Harris, Newman, Satterfield, Koster and Simonsick2013). Further, despite the increased risk posed to B/AA older adults for developing dementia, B/AA adults are largely underrepresented in research seeking to understand these diseases. There is also evidence that a missed or delayed diagnosis of AD and other dementia types is more common among B/AA older adults than among White older adults (Clark et al., Reference Clark, Kutner, Goldstein, Peterson-Hazen, Garner, Zhang and Bowles2005; Gianattasio et al., Reference Gianattasio, Prather, Glymour, Ciarleglio and Power2019; Lin et al., Reference Lin, Daly, Olchanski, Cohen, Neumann, Faul and Freund2021), which then contributes to a delay of care that may impact disease trajectory and outcomes. Thus, it is increasingly important to identify people at risk for AD and related dementias as early as possible, in part through accurately identifying individuals with mild cognitive impairment (MCI). A diagnosis of MCI refers to cognitive decline that is not normal for a person’s age but generally does not affect that person’s ability to carry out most activities of daily living (Gauthier et al., Reference Gauthier, Reisberg, Zaudig, Petersen, Ritchie, Broich and Winblad2006). MCI is classified as one of two types based on a person’s symptoms: amnestic (memory issues predominate) or non-amnestic (other cognitive issues predominate; Petersen et al., Reference Petersen, Lopez, Armstrong, Getchius, Ganguli, Gloss and Rae-Grant2018; Alzheimer’s Association, 2022). It is estimated that 10–15% of individuals with MCI go on to develop a form of dementia each year (Alzheimer’s Association, 2022) and about 1/3 of people with MCI develop dementia due to AD within five years (Alzheimer’s Association, 2022; 42). Others with MCI may revert to their preclinical cognition or remain clinically stable (Pandya et al., Reference Pandya, Clem, Silva and Woon2016).
Traditionally, neuropsychological measures have been used in clinical settings and in research studies to identify and track those with cognitive decline. However, more recently introduced computerized measures have a relative ease of administration when compared to traditional neuropsychological methods (Diaz-Orueta et al., Reference Diaz-Orueta, Blanco-Campal, Lamar, Libon and Burke2020; Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013). Consequently, computerized assessments will likely be in increasing demand. While traditional neuropsychological methods have been well studied, less is known about practice effects and the retest reliability of computerized testing methods, particularly with different racial/ethnic groups (Diaz-Orueta et al., Reference Diaz-Orueta, Blanco-Campal, Lamar, Libon and Burke2020; Scott et al., Reference Scott, Sorrell and Benitez2019). Practice effects refer to the expected and common improvement in test performance due to repeated exposures to test materials (Calamia et al., Reference Calamia, Markon and Tranel2012; Portney & Watkins, Reference Portney and Watkins2009). Retest reliability can be defined as the extent to which a measurement is consistent and free of random measurement error (the fluctuation in scores of repeated assessments due to unpredictable factors; Portney & Watkins, Reference Portney and Watkins2009). Retest reliability is essential to help clinicians and researchers understand how much of a measured change in score is attributable to measurement error and how much represents a true condition (Calamia et al., Reference Calamia, Markon and Tranel2012). Further, practice effects can mask actual cognitive decline in longitudinal studies of older adults and thereby give the illusion of stability or only minor change (Calamia et al., Reference Calamia, Markon and Tranel2012). Reliable change can be used to assess whether a change at retest on a given variable is “reliable” (meaning it is statistically improbable that the change is due to measurement error) and therefore represents a meaningful change (Chelune et al, Reference Chelune, Naugle, Luders, Sedlak and Awad1993; Iverson, Reference Iverson2001).
As more studies begin to incorporate computerized cognitive measures into clinical trials and longitudinal research, and scientists and clinicians explore the clinical applications of these tools, it becomes increasingly important to better understand reliability and retest issues for these methods. To ensure that measures and treatments are valid and reliable for B/AAs, B/AAs must be included in the research exploring these subjects. NIH Toolbox-Cognition Battery (NIHTB-CB) and the Cogstate Brief Battery (Cogstate) are computerized cognitive assessment batteries frequently used in clinical research. In a previous study the NIHTB-CB has been shown to have retest concordance correlation coefficients in healthy older adults ages 60–80 years of .73 for the Fluid Composite and .92 for the Crystallized Composite with individual subtests ranging between .46 and .88 (Scott et al., Reference Scott, Sorrell and Benitez2019). The NIHTB-CB has been shown to have interclass correlations in healthy adults ages 20–85 years of .79 for the Fluid Composite and .92 for the Crystallized Composite (Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014) with individual subtests ranging between .72 and .94 (Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013). The Cogstate individual subtest retest reliability using interclass correlations has been shown to range from .22 to .94 with healthy adults aged 18–96 years (Cole et al., Reference Cole, Arrieux, Schwab, Ivins, Qashu and Lewis2013; Faletti et al., Reference Falleti, Maruff, Collie and Darby2006; Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010; Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013), .79–.95 for those with amnestic MCI aged 60–96 years, and .68–.93 for those with Alzheimer’s disease aged 60 to 96 (Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013).
Despite the findings that older B/AAs are disproportionately more likely than older Whites to have all type dementia, previous studies examining the retest reliability of NIHTB-CB and Cogstate for laptop were conducted using mostly White samples (Cole et al., Reference Cole, Arrieux, Schwab, Ivins, Qashu and Lewis2013; Faletti et al., Reference Falleti, Maruff, Collie and Darby2006; Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010; Hammers et al., Reference Hammers, Spurgeon, Ryan, Persad, Heidebrink, Barbas and Giordani2011; Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014; Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013; Scott et al., Reference Scott, Sorrell and Benitez2019; Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013). Thus, the current study aimed to assess the retest reliability of NIHTB-CB and the Cogstate Brief Battery for laptop up to 4 months in healthy controls and those with MCI in a B/AA sample. The differences in scores between testing timepoints were calculated to examine practice effects and provide cut scores to determine reliable change. The relationship between testing interval and performance was also examined.
It was hypothesized that the NIHTB-CB retest reliabilities for the healthy controls in an all B/AA sample would be similar to previous findings using non-impaired majority White samples (Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014; Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013), as the scores used were a priori adjusted for age, sex, race/ethnicity, and education (available through NIHTB-CB for laptop). We hypothesized that the Crystallized Composite would be more reliable than the Fluid Composite, but that all three composites, and the subtests that comprise them, would demonstrate moderate to excellent reliability and small to medium practice effects up to 4 months in healthy controls. Less is known about the retest reliability and practice effects in those with MCI or AD when using the NIHTB-CB; however, those with MCI have demonstrated significantly attenuated learning performance on accuracy and reaction time tasks with repeated computerized testing when compared to healthy controls (Darby et al., Reference Darby, Maruff, Collie and McStephen2002). Thus, we hypothesized that those with MCI would demonstrate moderate to excellent reliability but be less susceptible to practice effects than healthy controls, particularly on Fluid tasks requiring a memory component. Less is known about the impact of shorter versus longer test intervals in the NIHTB-CB for either healthy controls or those with MCI; thus, findings should be viewed as exploratory.
On the Cogstate, no demographic-adjusted norms have been provided by the manufacturers. Still, it was hypothesized that all subtests would demonstrate moderate to excellent reliability and small to medium practice effects in healthy controls up to 4 months in an all B/AA sample based on the performance of majority White samples in prior studies (Cole et al., Reference Cole, Arrieux, Schwab, Ivins, Qashu and Lewis2013; Faletti et al., Reference Falleti, Maruff, Collie and Darby2006; Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010; Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013). Based on a previous study with a majority White sample, it was hypothesized that those with MCI would demonstrate similar retest reliability and susceptibility to practice effects as healthy controls on Cogstate subtests (Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013). Results have been mixed in studies exploring the length between retest intervals in Cogstate (Faletti et al., Reference Falleti, Maruff, Collie and Darby2006; Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010; Hammers et al., Reference Hammers, Spurgeon, Ryan, Persad, Heidebrink, Barbas and Giordani2011), so no a priori prediction was made.
Method
Participants
Participants were recruited through the Healthy Black Elders Center, the community engagement core for the Michigan Center for Urban African American Aging Research, a joint program through the Wayne State University Institute of Gerontology and the University of Michigan Institute of Social Research, and through the Michigan Alzheimer’s Disease Research Center (MADRC). This research was completed in accordance with the Helsinki Declaration. This study was reviewed and approved by the human subjects Institutional Review Board at Wayne State University in Detroit, MI, USA, and the human subjects Institutional Review Board at the University of Michigan Medical School in Ann Arbor, MI, USA. Participants were evaluated for decision making capacity at the time of the informed consent process. All participants signed consent as per the human subjects Wayne State University Institutional Review Board in Detroit, MI, USA, and the human subjects University of Michigan Medical School Institutional Review Board in Ann Arbor, MI, USA, prior to participation in the study. All participants completed the National Alzheimer’s Coordinating Center (NACC) – Uniform Data Set (UDS) version 2 evaluation which included a multidomain medical, neurological, social, and neuropsychological evaluation; participants were then diagnosed at the MADRC using NACC consensus conference criteria (Weintraub et al., Reference Weintraub, Salmon, Mercaldo, Ferris, Graff-Radford, Chui and Morris2009). NIHTB-CB and Cogstate results were not available to the consensus panel. The initial NIHTB-CB and Cogstate assessments were conducted up to 8 days before UDS visits and up to 117 days after UDS assessments with 71.1% of assessments taking place on the same day. The NIHTB-CB and Cogstate retest was conducted between 6 and 139 days (or within 4 months) after the initial administration with the mean being 46.9 days and the median being 33 days. Participants also completed a Computer Anxiety Survey (Wild et al., 2012) to assess their level of comfort with computers.
Participants were B/AA community-dwelling older adults between 60 and 85 years of age that reported having either male or female biological sex. Participants included in the analyses completed NIHTB-CB and Cogstate at two testing timepoints within four months of the initial administration and were classified by consensus diagnosis (Weintraub et al., Reference Weintraub, Salmon, Mercaldo, Ferris, Graff-Radford, Chui and Morris2009) as either having no clinically significant cognitive impairment (healthy control; n = 49) or as having MCI (n = 34). Those with MCI were further classified at consensus as MCI with amnestic features (aMCI; n = 24) or MCI with non-amnestic features (naMCI; n = 10). Due to the low incidence of naMCI observed in this sample and the statistical equivalence on demographic variables (see Results section, Demographics) to those with aMCI, the aMCI and naMCI subsamples were combined and are described hereafter as the MCI group (n = 34).
Assessment measures
National Institutes of Health Toolbox-Cognition Battery (NIHTB-CB): The NIHTB-CB was designed to be a brief (30-min), computerized, widely accessible, and easily administered cognitive screener for ages 3–85 that is available in both English and Spanish (Gershon et al., Reference Gershon, Wagster, Hendrie, Fox, Cook and Nowinski2013). It was originally designed for the purpose of creating a “common currency” among different research studies (Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013). The battery consists of seven tests measuring five cognitive domains, which are separated broadly into “fluid” or dynamic thinking skills (executive functions, episodic memory, processing speed, working memory) and “crystallized” or skills that remain relatively stable in adulthood (language – vocabulary knowledge and oral reading proficiency; Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014; Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013). Individual subtest performances as well as composite summary scores of crystallized cognitive abilities, fluid cognition, and total cognition are provided. The Crystallized Cognition Composite includes the Oral Reading Recognition and Picture Vocabulary subtests. Measures of fluid abilities include the Dimensional Change Card Sort task, Flanker Inhibitory Control and Attention, List Sorting Working Memory, Pattern Comparison Processing Speed, and Picture Sequence Memory subtests. Specific test details, procedures, and extensive psychometric evaluation are available elsewhere (Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013).
Cogstate Brief Battery (Cogstate): Cogstate is a computerized cognitive assessment that provides measures of four different cognitive domains using playing card paradigms: visual learning, working memory, processing speed, and attention. Briefly, the core tests include a Detection Task (a simple reaction time task), Identification Task (a choice reaction time test of visual attention), One Card Learning Task (a continuous visual recognition learning task), and One Back Task (a test of working memory). These separate tests and their psychometric properties have been described previously (Falleti et al., Reference Falleti, Maruff, Collie and Darby2006; Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013; Maruff et al., Reference Maruff, Lim, Darby, Ellis, Pietrzak, Snyder and Masters2013).
Computer Anxiety Survey: Computer anxiety was measured using the Wild et al. (Reference Wild, Mattek, Maxwell, Dodge, Jimison and Kaye2012) Computer Anxiety Survey, a 16-item measure on which participants rate their level of anxiety when using computers (e.g., “I feel relaxed when I am working on a computer”). Responses are rated on a five-point, Likert-type scale and range from “Strongly Disagree” to “Strongly Agree.” Total scores range from 16 to 80, with higher scores indicating greater levels of computer anxiety. Computer anxiety summary scores are derived by totaling the rating for each item. Specific survey details and psychometric properties have been described previously (Wild et al., Reference Wild, Mattek, Maxwell, Dodge, Jimison and Kaye2012).
Mini-Mental State Examination (MMSE): The MMSE is a brief objective measure of cognitive functioning that quantitatively estimates the severity of cognitive impairment (Folstein et al., 1975). However, it is important to note that the MMSE is a brief cognitive screening tool and it is not meant to be used as a means of diagnosis, but rather as a path to referral for more comprehensive testing if needed (Arevalo-Rodriguez et al., Reference Arevalo-Rodriguez, Smailagic, Roqué-Figuls, Ciapponi, Sanchez-Perez, Giannakou and Cullum2021; Ranson et al., Reference Ranson, Kuźma, Hamilton, Muniz-Terrera, Langa and Llewellyn2019; Tombaugh & McIntyre, Reference Tombaugh and McIntyre1992). The MMSE can typically be administered in 5–10 minutes. It consists of a variety of questions with total scores ranging from 0 to 30, and lower scores representing poorer cognitive function. Cut scores <24 and<25 are the most commonly used to suggest possible cognitive impairment (Tsoi et al., Reference Tsoi, Chan, Hirai, Wong and Kwok2015); however, a cut score of 27 (≤26) has been shown to maximize the diagnostic accuracy of the MMSE in B/AA individuals with more education (Spering et al., Reference Spering, Hobson, Lucas, Menon, Hall and O’Bryant2012). Specific test details, procedures, and psychometric evaluations have been compiled in the form of review articles (Tombaugh & McIntyre, Reference Tombaugh and McIntyre1992; Tsoi et al., Reference Tsoi, Chan, Hirai, Wong and Kwok2015).
Statistical analyses
All statistical analyses were conducted using SPSS V.28. Scores used in the analyses for NIHTB-CB were a priori adjusted (age, sex, race/ethnicity, and education) t-scores (M = 50, SD = 10) available through NIHTB-CB for laptop. Per manufacturer recommendations, scores used in the analyses for Cogstate were log transformed and derived from raw scores available through Cogstate; specifically, accuracy scores (correct vs. incorrect responses) for One Card Learning and One Back, and reaction time (in milliseconds) for Detection and Identification. Prior to analysis, all measures were screened for univariate and multivariate outliers and seven individual scores were identified as being extreme outliers (i.e., z-score >3.29) across measures. These seven individual outlying scores were then winsorized and subsequently included in the analyses. Accuracy scores for One Back were noted to be negatively skewed; specifically, healthy controls were found to be quite accurate when performing the One Back test. Those with MCI were also skewed negatively, but to a lesser extent.
Demographics
Demographic data were examined for group differences using independent measures t-test on continuous variables and chi-square statistics on categorical variables (see Table 1 for sample characteristics).
Sample characteristics
Note: Chi-square test was used for categorical variables and t-scores for continuous variables. HC = healthy controls; MCI = mild cognitive impairment; MMSE = Mini-Mental State Evaluation; p-value = level of significance; M/SD = mean/standard deviation; n = number of participants.
Intraclass correlation coefficients
To assess the degree of correlation and the agreement between measurement timepoints (or retest reliability), two-way mixed intraclass correlation coefficients (ICCs) with 95% confidence intervals (CIs) were run using an absolute definition of agreement for the total sample, healthy controls, and those with MCI (McGraw & Wong, Reference McGraw and Wong1996). ICCs were interpreted using the guidelines set forth by Koo and Li (2016) who recommended that CIs be interpreted rather than the singular correlation coefficient and defined values less than .40 as low reliability, values between .40 and .74 as moderate, values between .75 and .89 as good, and values greater than .89 as excellent (Table 2, Table 3).
Intraclass correlation coefficients examining retest reliability up to 4 months for NIH Toolbox-Cognition Battery and Cogstate Brief Battery for laptop
Note: Scores used were the norm a priori adjusted (age, sex, race/ethnicity, and education) t-scores (M = 50, SD = 10) available through NIH Toolbox-Cognition Battery for laptop and log-transformed scores derived from raw scores for Cogstate Brief Battery for laptop. ICC = intraclass correlation coefficient; CI = confidence interval; p-value = level of significance; RT = reaction time.
Paired-sample t-tests examining practice effects for NIH Toolbox-Cognition Battery and Cogstate Brief Battery for laptop
Note: Scores used were the norm a priori adjusted (age, sex, race/ethnicity, and education) t-scores (M = 50, SD = 10) available through NIH Toolbox-Cognition Battery for laptop and log-transformed scores derived from raw scores for Cogstate Brief Battery for laptop. t = t-test statistic; M(SD)1 = M = mean of testing timepoint 1, SD = standard deviation of testing timepoint 1; M(SD)2 = M = mean of testing timepoint 2, SD = standard deviation of testing timepoint 2; r 12 = Pearson correlation between timepoint 1 and timepoint 2; p-value = level of significance; d = Cohen’s measure of sample effect size; RT = reaction time.
Bland-Altman method
The Bland-Altman method was used to test for changes in the mean between the two test occasions and inspect for systematic bias and limits of agreement. Specifically, mean differences using the Bland-Altman method and 95% estimation of CIs for limits of agreement were calculated (Bland & Altman, 1995); bias was defined as having all observations lie to one side of the line of equality (i.e., observations that did not include zero (the line of equality) within the 95% CI) (Table 4).
Values used to calculate reliable change between timepoints 1 and 2 for NIH Toolbox-Cognition Battery and Cogstate Brief Battery for laptop
Note: Scores used were the norm a priori adjusted (age, sex, race/ethnicity, and education) t-scores (M = 50, SD = 10) available through NIH Toolbox Cognition Battery for laptop and log transformed scores derived from raw scores for Cogstate Brief Battery for laptop. SEM1 = standard error of measurement at testing timepoint 1; SEM2 = standard error of measurement at testing timepoint 2; M(SD)diff = with M being the mean difference and SD being the standard deviation of difference; SE = standard error of difference; RT = reaction rime.
Paired sample t-tests
Paired sample t-tests with 95% CIs were used to evaluate for practice effects upon retest in the total sample, healthy controls, and those with MCI. Cohen’s d was used to measure effect size and results were interpreted using benchmarks suggested by Cohen (Reference Cohen1988) with ±0.2 as small, ±0.5 as medium, and ±0.8 as large (Table 5).
Reliable change confidence intervals for NIH Toolbox-Cognition Battery and Cogstate Brief Battery for laptop
Note: Scores used were the a priori norm adjusted (age, sex, race/ethnicity, and education) t-scores (M = 50, SD = 10) available through NIH Toolbox Cognition Battery for laptop and log transformed scores derived from raw scores for Cogstate Brief Battery for laptop. Confidence intervals (CI) were calculated by multiplying the Standard Error of Difference of performance on testing timepoint 1 and testing timepoint 2 by a z-score to arrive at the confidence intervals (70%, 80%, 90%). If a retest score for a given variable changes by the provided amount or more (either positive or negative), that score is indicative of worsening or improvement. For example, a person whose score has gotten worse with retesting by the amount shown above (or greater) for a given variable, would be exceeding the worsening in scores experienced by 85, 90, or 95% of the sample, respectively. CI = Confidence Interval; RT = reaction time.CI = confidence interval.
Reliable change
Reliable change methodology was used to calculate reliable change CIs that can be used to assess whether a change in score after retest is reliable and meaningful (Chelune et al, 1993, Iverson, 2001). Specifically, the standard error of difference score (SEdiff) was calculated using the standard error of measurement at initial testing (SEM1) and at retest (SEM2) as per guidelines set by Iverson (2001). That is: SEdiff = √((SEM1)2 + (SEM2)2), or SEdiff = √((SD1√1-r 12)2 + (SD2√1-r 12)2), with SD1 and SD2 referring to the standard deviation at test and retest, respectively, and r 12 referring to test-retest correlation between time 1 and time 2. The SE diff was then multiplied by a z-score to arrive at CIs (70, 80, 90%).
Reliable change CIs can be directly referenced to determine if a change in score after retest on a given variable is reliable and meaningful. For use, examiners calculate a difference score (i.e., t-score at retest minus t-score at initial testing) using their patient or participant score(s). Norm-adjusted t-scores provided by NIHTB-CB should be used to calculate these difference scores, and log-transformed raw scores should be used for Cogstate. Difference scores for a given variable that change by the amount presented in Table 6 or more (either positive or negative) are indicative of worsening or improvement. For example, a person who changes by the amount provided or greater would be exceeding the change in scores experienced by 85, 90, or 95% of the sample, respectively.
Correlation between difference scores and the difference in days between testing timepoints on the NIH Toolbox-Cognition Battery and Cogstate Brief Battery for laptop
Note: Scores used were the norm a priori adjusted (age, sex, race/ethnicity, and education) t-scores (M = 50, SD = 10) available through NIH Toolbox-Cognition Battery for laptop and log transformed scores derived from raw scores for Cogstate Brief Battery for laptop. Difference scores were calculated by subtracting the score from testing timepoint two from the score from timepoint one for a given variable per individual participant. Pearson correlation coefficient was used to evaluate the correlation between testing timepoint difference scores and the difference in days between testing timepoints. r = Pearson correlation coefficient; p-value = level of significance; RT = reaction time.
Pearson correlation coefficient
The relationship between testing interval and performance was examined using the Pearson correlation coefficient. Specifically, the testing performance difference scores (derived by subtracting testing timepoint two from timepoint one for a given variable) and the length in days between testing timepoints were used. Results were interpreted using benchmarks suggested by Cohen (Reference Cohen1988) with ±0.1 as small, ±0.3 as medium , and ±0.5 as large.
Results
Demographics
Sample characteristics for the total sample, healthy controls, and those with MCI (combined variable aMCI and naMCI) can be found in Table 1. All participants identified as B/AA. No significant differences were found between healthy controls and those with MCI for age, sex, or level of computer anxiety. A significant difference was found between healthy controls and those with MCI for education, with healthy controls having approximately one more year of schooling on average than those with MCI. A significant difference in the total Mini-Mental State Exam score between healthy controls and those with MCI was found; specifically, healthy controls scored approximately one point higher on average than those with MCI. While there was no significant difference between healthy controls and those with MCI in terms of sex, the majority of the sample was female. Those with a diagnosis of aMCI were compared to those with a diagnosis of naMCI on demographic variables and no significant differences were noted in age, sex, or education between the two groups. Additionally, there was no statistical difference between those with aMCI versus those with naMCI on the Mini-Mental State Examination total score or level of computer anxiety in our sample.
NIHTB-CB
Intraclass correlation coefficients
When examining retest reliabilities in the total sample, healthy controls, and those with MCI on the NIHTB-CB, the Crystallized Composite (ICCs = .87–.93, 95% CIs [.74–.96]) demonstrated the highest reliability followed by the Total Composite (ICCs = .81–.91, ICs [.42–.96]) and the Fluid Composite, respectively (ICCs =.81–.85, ICs [.37–.93]; see Table 2). The reliability of individual NIHTB-CB measures for the total sample varied from moderate to excellent. For healthy controls, the individual tests comprising the Crystallized Composite both demonstrated good to excellent reliability and were more consistent (both ICCs = .86, CIs [.75–.92]) when compared to the Fluid Composite which ranged from moderate to good (ICCs = .69–.85, CIs [.46–.92]). Individual subtests were less consistent within and across the composites for those with MCI, with reliabilities ranging from low to excellent (ICCs = .01–.89, CIs [−1.00–.95]). Retest reliabilities were also calculated for aMCI and naMCI separately. Though caution must be stated given the lack of power, we found that reliabilities for those with aMCI and naMCI were similar on the Total Composite and Crystallized Composite, as well as many of the subtests comprising the Composites. However, those with aMCI were somewhat less reliable than those with naMCI on Picture Vocabulary (aMCI ICC = .66; naMCI ICC = .81) and much less reliable on List Sorting Working Memory (aMCI ICC = .15; naMCI ICC = .63) and Picture Sequence Memory (aMCI ICC = .10; naMCI ICC = .64). Thus, it appears that those with aMCI were driving the poor reliabilities on these measures reported in Table 2.
Bland-Altman method
Applying the Bland-Altman method to the three NIHTB-CB composites revealed that for the total sample, healthy controls, and those with MCI only the Crystallized Composite was not significant, and the 95% CI included zero; these findings indicated that there was no proportional or systematic bias in the Crystallized Composite across samples. Of the seven individual NIHTB-CB subtests Picture Vocabulary, Oral Reading Recognition, and List Sorting Working Memory were the only subtests that were not significant and included zero within the 95%CI when applying the Bland-Altman method to the total sample and healthy controls. For those with MCI, the pattern was similar, but Picture Sequence Memory was also not significant and included zero within the 95% CI for those with MCI.
Paired sample t-tests
On the paired sample t-tests comparing NIHTB-CB testing timepoint two to testing timepoint one, the Total Composite and the Fluid Composite were significant for the total sample, healthy controls, and those with MCI (see Table 3); this demonstrates significant practice effects (through an increase in scores) with medium effect sizes. Conversely, the NIHTB-CB Crystallized Composite was not significant across samples and had small effect sizes, thereby demonstrating less susceptibility to practice effects. Of the individual NIHTB-CB subtests, the two subtests comprising the Crystallized Composite (Picture Vocabulary and Oral Reading Recognition) were not significant across samples and had small effect sizes. In the total sample and the healthy controls, all Fluid Composite subtests, except List Sorting Working Memory (which was insignificant and had small effect sizes), did differ significantly; specifically, improved performances (or practice effects) were seen on Flanker Inhibitory Control and Attention, Dimensional Change Card Sort, Pattern Comparison Processing Speed, and Picture Sequence Memory for healthy controls. For those with MCI, the fluid measures Flanker Inhibitory Control and Attention, Dimensional Change Card Sort, and Pattern Comparison Processing Speed did significantly improve, while Picture Sequence Memory and List Sorting Working Memory did not differ significantly between testing timepoints. Effect sizes for fluid subtest measures across samples ranged from small to medium.
Reliable change
Values used to calculate reliable change betweens timepoint 1 and 2 are listed in Tables 3 and 4. Reliable change CIs that can be used as cut scores to interpret reliable change are provided in Table 5. See the Statistical Analysis section of this paper for more information regarding the interpretation of reliable change.
Pearson correlation coefficient
Table 6 shows correlations between difference in days between test administrations and difference in testing performance between testing timepoints for the total sample, healthy controls, and those with MCI on the NIHTB-CB. Though the Fluid Composite and Pattern Comparison Processing Speed were considered significant at the < .05 level in the total sample, a low degree of correlation was noted with correlations ranging from −.28 to .06. For healthy controls, no correlations were significant, and correlations ranged from −.27 to .21. All correlations for those with MCI were negative and many were low and not significant (ranging from −.18 to −.09), but a moderate degree of correlation was found between the difference in days between administrations and difference in testing performance between testing timepoints for the NIHTB-CB Total Composite, Fluid Composite, and Pattern Comparison Processing Speed.
Cogstate
Intraclass correlation coefficients
When examining retest reliabilities for the total sample, healthy controls, and those with MCI, the individual Cogstate subtests had CIs that ranged from low reliability to excellent (see Table 2). Healthy controls demonstrated consistently lower retest reliability (ranging from low to good reliability (ICCs = .47–.76, CIs [.05–.86])) relative to those with MCI (ranging from low to excellent (ICCs = .65–.89, CIs [.29–.95])) on all Cogstate subtests. One Back Accuracy was the least reliable of the Cogstate measures for both those with MCI and healthy controls. An ad hoc analysis of One Back reaction time was conducted due to the skewed distribution of the One Back Accuracy subtest. It was found that One Back reaction time had a more normal distribution and significant reliabilities (all p = < .001) ranging from moderate to excellent across samples (ICCs = .82–.84, CIs [.63–.91]; see Table 2). Reliabilities were also calculated for naMCI and aMCI separately. Though caution must be stated given the lack of power, we found that those with aMCI demonstrated similar reliabilities on Cogstate subtests with the exception of One Back Accuracy (aMCI ICC = .58; naMCI ICC = .81) and One Back reaction time (aMCI ICC = .73; naMCI ICC = .91).
Bland-Altman method
Of the four subtests that comprise Cogstate, only Identification reaction time was significant and did not contain zero within the 95% CI when applying the Bland-Altman method across samples (total sample, healthy controls, MCI). This indicates that there was proportional or systematic bias in this subtest but not the other three subtests across samples.
Paired sample t-tests
When comparing Cogstate testing timepoint two to testing timepoint one using paired sample t-tests it was found that Identification reaction time differed significantly with medium effect sizes for the total sample, healthy controls, and those with MCI (see Table 3). Specifically, participants demonstrated practice effects when they completed the task faster on average on the second administration across samples. Healthy controls were found to significantly improve their performance on One Back reaction time, whereas those with MCI did not. Detection reaction time, One Card Learning Accuracy, and One Back Accuracy did not differ significantly between testing timepoints or across samples.
Reliable change
Values used to calculate reliable change between timepoints 1 and 2 are listed in Tables 3 and 4. Reliable change CIs that can be used as cut scores to interpret reliable change are provided in Table 5. See the Statistical Analysis section of this paper for more information regarding the interpretation of reliable change.
Pearson correlation coefficient
A low degree of correlation was noted in the total sample, for healthy controls, and those with MCI when correlating difference in days between test administrations and difference in testing performance between testing timepoints (see Table 4). Correlations ranged from r = -.23 to .28 and none were significant.
Discussion
NIHTB-CB
Across samples (total sample, healthy controls, those with MCI), the NIHTB-CB composite scores demonstrated good to excellent reliability up to 4 months in a B/AA sample. These findings are similar to prior research using healthy adults ages 20 to 85 with a majority White sample that retested participants between 7 and 21 days (Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014). As hypothesized, and consistent with previous research, the NIHTB-CB Crystallized Composite was found to be more stable between testing timepoints and across samples when compared to the Fluid Composite (Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014; Scott et al., Reference Scott, Sorrell and Benitez2019). The Crystallized Composite was also shown to be the only composite free of systematic bias across samples when applying the Bland-Altman method (Bland & Altman, Reference Bland and Altman1995). These findings were not unexpected as measures of vocabulary and reading are often found to be less susceptible to cognitive changes and age in adulthood (Heaton et al., Reference Heaton2004).
Consistent with a previous finding using healthy adults ages 20 to 85 with a majority White sample that retested participants between 7 and 21 days, the NIHTB-CB individual subtests demonstrated moderate to excellent reliability for the total sample and healthy controls (Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013) but were less consistent for those with MCI. Healthy controls were the most reliable on crystallized measures and the least reliable on fluid skills – adding further support to previous findings describing “fluid” skills on the NIHTB-CB as more susceptible to practice effects in healthy adults than “crystallized” skills (Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014; Scott et al., Reference Scott, Sorrell and Benitez2019). Those with MCI demonstrated a similar pattern on composites. However, those with MCI showed greater variability than healthy controls when looking at the reliabilities of individual tests that make up the composites. For example, those with MCI demonstrated poor reliabilities and no significant benefit with retesting on a task of working memory and a test of episodic memory. While this finding is not consistent with our hypothesis that those with MCI would demonstrate moderate to excellent reliabilities, it is consistent with our hypothesis that those with MCI would be less susceptible to practice effects than healthy controls. The poorer reliabilities seen in those with MCI compared to healthy controls may be due to the heterogeneity of the sample. For example, our preliminary findings showed that those with aMCI were less reliable than those with naMCI on the working memory and episodic memory tasks in particular – suggesting that those with aMCI were driving the poor reliabilities found on these measures. Working memory deficits have been seen in both aMCI and naMCI when compared to healthy controls (Saunders & Summers, Reference Saunders and Summers2010; Klekociuk & Summers, Reference Klekociuk and Summers2014). However, differences between those with aMCI and naMCI on working memory tasks may depend on the type of working memory task and the level of impairment of the individual (Klekciuk & Summers, Reference Klekociuk and Summers2014). In a recent study exploring how well the NIHTB-CB and Cogstate differentiate those with aMCI from those with naMCI, working memory was not a significant predictor of disease type (Garcia et al., Reference Garcia, Askew, Kavcic, Shair, Bhaumik, Rose and Giordani2023). Thus, our finding that a task of working memory was notably less reliable for those with aMCI than for those with naMCI should be viewed with caution as it may be due to individual differences in our sample or the relatively low sample size of those with naMCI. The finding that those with aMCI were less reliable than both healthy controls and those with naMCI on an episodic memory task is unsurprising, as memory is one of the earliest cognitive domains negatively impacted by cognitive decline (Bastin & Salmon, Reference Bastin and Salmon2014) and predominate memory dysfunction is the criteria that differentiates those with aMCI from those with naMCI (Peterson et al., Reference Petersen, Lopez, Armstrong, Getchius, Ganguli, Gloss and Rae-Grant2018).
We found that the length of testing interval was not significantly associated with changes between testing timepoints for healthy controls on the NIHTB-CB up to 4 months. Though not significant, we did find that as length of test interval increased, the association with test performance decreased on all fluid measures across samples. For those with MCI, the association decreased on crystallized measures as well, though not significantly. The only significant finding was that, for those with MCI, the association between performance on a visual processing speed test significantly decreased as time between testing intervals increased. These findings are generally consistent with previous findings showing as length of time increases the difference between scores decreases (Calamia et al., Reference Calamia, Markon and Tranel2012; Hausknecht et al., Reference Hausknecht, Halpert, Di Paolo and Moriarty Gerrard2007; Salthouse et al., Reference Salthouse2004; Scharfen et al., Reference Scharfen, Blum and Holling2018).
Cogstate
We found that all subtests demonstrated moderate to good reliability in the total sample up to 4 months in a B/AA sample, which is generally consistent with prior research using predominantly White samples (Cole et al., Reference Cole, Arrieux, Schwab, Ivins, Qashu and Lewis2013; Faletti et al., Reference Falleti, Maruff, Collie and Darby2006; Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010; Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013). Similar to prior research with a majority White sample aged 60–96 years that compared healthy older adults to those with MCI and Alzheimer’s dementia with retesting at 1, 2, and 3 months (Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013), we found that healthy controls demonstrated lower retest reliability than those with MCI on all Cogstate subtests. Further, the finding that One Back Accuracy was the least reliable of the Cogstate measures for both healthy controls (Falleti et al., Reference Falleti, Maruff, Collie and Darby2006; Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013) and those with MCI was replicated (Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013). As hypothesized, healthy controls and those with MCI demonstrated similar practice effect profiles upon retest. Though participants did not significantly improve on a simple reaction time task, a measure of choice reaction time did show significant improvement across samples. This finding is not surprising considering that reaction time is generally known to improve with practice and larger improvements are observed in more complex mental speed tasks compared to simple ones (Scharfen et al., Reference Scharfen, Blum and Holling2018). Though caution should be used in interpretation due to a lack of power, preliminary findings demonstrated similar reliabilities on Cogstate subtests for both subtypes of MCI, apart from accuracy on a memory task. Retest reliability was poorer for those with aMCI on this memory task than for healthy controls and those with naMCI. This finding is consistent with the greater decline in memory expected in those with aMCI relative to both unimpaired individuals and those with naMCI (Bastin & Salmon, Reference Bastin and Salmon2014; Peterson et al., Reference Petersen, Lopez, Armstrong, Getchius, Ganguli, Gloss and Rae-Grant2018).
During the preliminary data analysis, One Back Accuracy was noted to have a negatively skewed distribution across samples with most performances in the highly accurate range – thereby demonstrating a ceiling effect and less opportunity for change. An ad hoc analysis was conducted evaluating One Back reaction time instead of Accuracy, and reaction time was found to have a more normal distribution and produced better reliabilities. Similarly, a previous study with cognitively healthy adults reported consistently better reliabilities for One Back reaction time versus One Back Accuracy (Falleti et al., Reference Falleti, Maruff, Collie and Darby2006), and another study of cognitively healthy older adults found excellent reliability when using One Back reaction time instead of One Back Accuracy (Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010). This suggests that One Back reaction time may be a more reliable measure when using Cogstate over multiple testing timepoints despite the manufacturer’s recommendation to use One Back Accuracy.
The amount of time between testing timepoints did not appear to be significantly related to performance on Cogstate across samples up to 4 months. This finding is consistent with a study that did not find a significant change in performance for healthy controls, those with MCI, or those with AD across 1-, 2-, and 3-month retest intervals (Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013), and a study of healthy controls that did not find a significant difference in reaction time on the Detection subtest at a 10-minute retest interval or a 7-day retest interval (Falleti et al., Reference Falleti, Maruff, Collie and Darby2006). Conversely, this finding was not consistent with a study of healthy controls that found a small improvement in group performance for accuracy of performance in the One Card Learning task from baseline to the 3-month retest that persisted on the 6-, 9-, and 12-month retests (Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010), and a study of healthy controls that found significant improvement in One Back Accuracy at a 10-min retest interval and at a 7-day retest interval (Falleti et al., Reference Falleti, Maruff, Collie and Darby2006).
Considerations for both measures
Though NIHTB-CB composite scores demonstrated strong retest reliability, Cogstate subtests appeared less susceptible on the whole to retest effects than NIHTB-CB subtests (particularly fluid measures). Interestingly, those with MCI demonstrated better retest reliability than healthy controls on Cogstate, while healthy controls demonstrated higher retest reliability on the NIHTB-CB composites than those with MCI. This difference rests primarily on the lower retest reliability for those with MCI on a measure of working memory and a measure of episodic memory within the NIHTB-CB. With the exception of significant improvement on an episodic memory task for healthy controls and not for those with MCI with retest, practice effect profiles were similar on NIHTB-CB subtests. Cogstate reliabilities for healthy controls were lower across each subtest when compared to those with MCI, but the practice effect profiles were similar between healthy controls and those with MCI.
Reliable change methodology was used to assess whether a change in score after retest on a given variable is reliable and meaningful (Chelune et al, Reference Chelune, Naugle, Luders, Sedlak and Awad1993; Iverson, Reference Iverson2001). Reliable change CIs (70, 80, 90%) were provided for both NIHTB-CB and Cogstate. The resulting values serve as cutoffs indicative of reliable change that are easily translatable into research and clinical practice (indicating improvement, decline, or stability; Chelune et al, Reference Chelune, Naugle, Luders, Sedlak and Awad1993; Iverson, Reference Iverson2001). That is, a person whose score changes by the amount (or greater) provided in Table 5 for a given variable, would be exceeding the change in scores experienced by 85, 90, or 95% of the sample, respectively. For example, if a clinician or researcher used the 70% CI, a change score exceeding the cutoff would indicate a greater change than in 85% of the present sample. The greater the CI value the more conservative (or higher specificity). Practice effects can also be taken into account if desired (see Chelune et al., Reference Chelune, Naugle, Luders, Sedlak and Awad1993).
Limitations and future directions
One limitation of this study is that we were not able to include older adults over the age of 85 due to a lack of norms in the field for this age group. Other studies such as the Advancing Reliable Measurement in Alzheimer’s Disease and Cognitive Aging Study are addressing this problem by extending the NIHTB norms to those over the age of 85 (Weintraub et al., Reference Weintraub, Karpouzian‐Rogers, Peipert, Nowinski, Slotkin, Wortman, Ho, Rogalski, Carlsson, Giordani, Goldstein, Lucas, Manly, Rentz, Salmon, Snitz, Dodge, Riley, Eldes and Gershon2022). Due to the low incidence of naMCI observed in this sample, those with aMCI and naMCI were combined into one group (MCI). Although we did not have enough participants with a diagnosis of naMCI to reliably make recommendations regarding the reliability, we did find differences between those with aMCI and naMCI on NIHTB-CB and Cogstate. We also found that the NIHTB-CB was better able to differentiate the two subtypes of MCI than Cogstate, which is consistent with a recent publication (Garcia et al., Reference Garcia, Askew, Kavcic, Shair, Bhaumik, Rose and Giordani2023). Future studies should attempt to recruit larger numbers of both classifications of MCI to reliably examine potential differences. Healthy controls had approximately one year more of education on average in our study than those with MCI. This finding is consistent with prior literature that has found higher education to be associated with less cognitive impairment (Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014; Meng & D’Arcy, Reference Meng and D’arcy2012; Mungas et al., Reference Mungas, Shaw, Hayes-Larson, DeCarli, Farias, Olichney and Mayeda2021). Additionally, our sample, as a whole, was highly educated (average greater than 14 years), and, thus, may not be generalizable to less educated individuals; however, it should be noted that scores used in the NIHTB-CB analyses were a priori norm-adjusted scores (age, sex, race/ethnicity, and education) provided by NIHTB-CB. When such norms are used appropriately, they offer greater diagnostic accuracy (Manly, Reference Manly2005) and have been recently shown to reduce the association between education and cognitive performance in a racially diverse sample (Mungas et al., Reference Mungas, Shaw, Hayes-Larson, DeCarli, Farias, Olichney and Mayeda2021). Though not unusual for aging research, there were significantly more females than males that participated in this study; future studies might attempt to recruit equal numbers of males and females to study possible sex effects more directly in the context of the reliability of these measures.
Conclusions
Despite findings that older B/AAs are disproportionately more likely than older Whites to have all type dementia (Dilworth-Anderson et al., Reference Dilworth-Anderson, Hendrie, Manly, Khachaturian and Fazio2008; Power et al., Reference Power, Bennett, Turner, Dowling, Ciarleglio, Glymour and Gianattasio2021; Steenland et al., Reference Steenland, Goldstein, Levey and Wharton2016; Yaffe et al., Reference Yaffe, Falvey, Harris, Newman, Satterfield, Koster and Simonsick2013), previous studies examining the retest reliability of NIHTB-CB and Cogstate for laptop were conducted using mostly White samples (Cole et al., Reference Cole, Arrieux, Schwab, Ivins, Qashu and Lewis2013; Faletti et al., Reference Falleti, Maruff, Collie and Darby2006; Fredrickson et al., Reference Fredrickson, Maruff, Woodward, Moore, Fredrickson, Sach and Darby2010; Hammers et al., Reference Hammers, Spurgeon, Ryan, Persad, Heidebrink, Barbas and Giordani2011; Heaton et al., Reference Heaton, Akshoomoff, Tulsky, Mungas, Weintraub, Dikmen and Gershon2014; Lim et al., Reference Lim, Jaeger, Harrington, Ashwood, Ellis, Stöffler and Maruff2013; Scott et al., Reference Scott, Sorrell and Benitez2019; Weintraub et al., Reference Weintraub, Dikmen, Heaton, Tulsky, Zelazo, Bauer and Gershon2013). Thus, this study provided retest reliabilities and reliable change cutoffs for an all B/AA sample of healthy controls and those with MCI. Though retest reliabilities found in this study are similar to previous findings using mostly White samples, differences were also noted. Differences were also found between healthy controls and those with MCI. It is, therefore, recommended that race and cognitive status be considered when using these measures. Overall, it was found that the NIHTB-CB and Cogstate for laptop both show promise for use in research with B/AA and were reasonably stable for up to 4 months.
Although differences in reliability were noted between the two measures, the choice between test measures should reflect multiple considerations and the needs of a particular study. For example, while Cogstate takes considerably less time to administer, it is heavily reliant on reaction time, and it does not offer the breadth of cognitive measurement as NIHTB-CB. NIHTB-CB also provides norm-adjusted scores for age, sex, race/ethnicity, and education, whereas Cogstate does not. Preliminary findings also demonstrated greater differentiation between MCI subtypes (aMCI and naMCI) when using the NIHTB-CB versus Cogstate.
Acknowledgments
Specific thanks are given to the reviewers for this article. Their input and recommendations helped to strengthen this paper.
Funding statement
This work was supported by the US National Institute on Aging (P30 AG053760, P30 AG072931) and the US National Institute of Health (V.K., R01 AG054484, R21 AG046637).
Competing interests
None.
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